s comparing and has quite a big improvement comparing to baseline I also compared character F metric which compares not word engrams but character engrams and sometimes is considered better and here I compared my results to other works on low resource languages but this is maybe not very representative because there is a lot of factors that affect the result of the model especially for different languages but just so that you can see how it generally works what is more representative though is examples of the sentences that the model gave to us you can see on the top example that actually the meaning of the resulting translation is like the same to the reference translation and in the below example you can see that sometimes the model still makes mistakes like here it missed the word to study basically and the future work it may include adjusting the number of byte pair encoding merging operations because some hypothesis includes that the more morphological the tokenization is maybe the model will train better also traditional way is to expand the corpus and also trying another Turkic or not Turkic language for the parent model so this is all I think and I am ready to answer your questions okay do we have a question we have three questions did you consider pre-training on some other languages to enable better transfer learning capabilities let's say you pre-trained on some other languages in unsupervised way which might boost performance for the corpus yes of course this is a future work we will try different languages and maybe adjust the NLB model whatever so that we can compare the results do you have any idea which languages are more beneficial less beneficial because some might hurt some might improve yes actually I read some articles about this and the main idea I think is to be similar in morphological structure because for example when you translate from English to Russian it will be the same word for all the cases and things like that and it is difficult for the model to catch those those differences so the more complicated I think the parent the language for the parent model the better for the child model since Hacass is very morphologically original but not necessarily the same linguistic family no actually there are there are studies that show that the size of the corpora makes the biggest difference so they even pre-trained on Finnish language and fine-tuned on Turkish something like that and they still managed to achieve some good results some colleagues had similar experiments and that was counter-intuitive to me but they pre-trained on some completely different language but with similar morphological structure and actually give a boost thank you I have a small question so are you familiar with Hacass language or is one of your colleagues knows it how did you evaluate the results I am not familiar with Hacass language but I know some Tatar and it is quite similar so I could actually visually evaluate it somehow but for the sake of science my translations were evaluated by the native speakers that I asked to do thank you thank you for the talk I have a couple just I think technical questions could you please maybe I've missed that just deliberate a bit more what kind of model did you use just transformer architecture with 6 encoder and 6 decoder layers and 8 attention I see okay and with blue right what kind of for blue evaluation what kind of tokenization did you use it was the same tokenization as for the training the VPE thing well that's interesting because maybe for the sake of I don't know getting a different view on the quality maybe you should try to I don't know try some some like raw tokenization by or some I'm not sure about the existence of morphological analyzer for Hacass language but something like standing on limitization and raw tokens because this can give a different perspective on the evaluation it's like the same issue that was widely discussed at the time character level machine translation was popular so it's a bit different thing so definitely worth comparing I think yes I think we can do that thank you for the talk I have not a question but rather a suggestion you have said that one of the corpora was in some other script in Latin probably maybe you could use just some transliteration scheme and augment the data that way yes as I said we wanted to be in Cyrillic because we thought first we used the shared vocabulary but the transliteration crossed our minds but I actually didn't find the good transliteration tool because for example for Turkish language it is very difficult it has many rules and to transliterate it to Cyrillic is a very complicated separate task so I didn't find the tool and I didn't go for this idea yeah as far as I know for some languages exist like specific transliteration scheme so if someone has developed it it could be used I will have to look at it, thank you okay more questions let me check with zoom okay we don't have questions zoom so thank you and the next talk will be online yes that's true nice to meet you, I am Zaitsever XA and I will be talking today about how difficult it is to make another Cyrillic attack for machine translation models I have two authors that did most part of the job, most part of the experiments in writing I am Pavel Burnushov and he is Veta Kostunov we are participating in this project as well so let's start on what and why we are doing so basically okay all models most of the models we see today in NLP neural networks and we often see some very rare examples on how they work if you know it's the right way to attack it how to make it do what you want and for this for example prohibited by the model authors so it seems like a good goal to try to examine and how can we find some big points of the model can you hear me let's say we can see the slides let's wait a little bit okay so what is the problem we want to find some vulnerable points of the model and we can try to find this with adversarial attacks so basically adversarial attacks are way to find this vulnerabilities we try to find in an efficient way on how can we break the model how we alter the output of the model by small enough change of the input for example we can see the following idea here is an example of an attack in the bottom so we have some models that tries to mark this input flat misguided comedy okay to a label in this case you can see it is definitely a negative review and so we try to fix this input this as the model changes possible so in this way in this example we change flat to which a basic reasoning is almost the same and in this case the model outputs positive label a marking that we succeed succeeded in breaking the model and now the label is not correct because definitely it is still not a positive review but model sync it is positive so the question is how should we design such attacks to common classification models for NLP or for example translation models so okay the problem maybe the main problem for this area is that we don't have differentiability and the search space for NLP is discrete and we somehow should move in the space looking for adversarial attacks, adversarial changes of the input basically what we can try to do we can try to for example in code flip calculate the gradient with respect to particular token in the input and so we can try to say that if you have this test order approximation of what is going on we can then find what and how we should change to change the loss most and what we are doing and what is a pretty successful strategy and also we can try to use modernized models this was proposed in our previous paper it is based on the idea that we somehow should modify the loss function when designing the adversarial attack so basically what we can try to do we have this input sequence x1 is on the right x1, x2, x3 and so on xd where d is the length of the sequence and we want to train a generative model or to modify it so now the output is some x prime, x1 prime x2 prime and so on and so what we want from this sequence we can really express in the loss function as a test is a classifier so we want the label of the output to be different from what we have for initial sequence and this we can encode with this second term here and we see capital is the score of model also we can try to say that okay we should not be far away and this the deep Levenstein distance or whatever other distance like below similarity we have we wanted the similarity of x prime and x to be high in terms of how many tokens we change or in terms of some semantics metrics and in this case we design the loss function and we can backpropagate this on the softmax trick into the generator and it generates adversarial this is what we can do and this also transfers problem from a discrete optimization problem to continuous optimization problem so we can try to adjust it and basically this idea is followed by another one that allow us to do practically the same thing but for machine translation model basically we try to find the change in the embedding space that leads to okay small change of the input but a significant change in the output so we look at this gradient this color product of the gradient of the adversarial loss and our embedding we try to find the embedding that is good and using this embedding it generates back decodes adversarial sequence and also change we hope significantly the output and this we can proceed in different ways for example we can say that okay we have another more okay we have this loss function and we focus on predicting below and below for the initial sequence should be high so we should not change x significantly but if you look at below for a pair of y and the adversarial translation by prime are short based significantly different and we can direct organize flow of the gradients in all parts of this model and train it to generate to find good candidates for adversarial attack so it seems like a very natural approach but does it work let's look at the results for classification problem can be changed with our generate deal model input and obtain good results and the thing is that yes we can and what we are looking for in this table we have four problems and we have the accuracy for these problems before attack the scores of the accuracy is pretty high it's like for example AG is 0.95 here but what's going on next after different attacks there are some sort of attacks and the last two approaches are different variants power attack and we see that if we use our top performance attack deal with this deep Levenstein distance we have for some problems almost 0.5 accuracy that means that we can almost rock the model here and here a significantly decrease the quality of the model for AG and FTE problems and so on what else we can look what we usually should check as well is that our change in the meaning is pretty small and we don't change much the semantics of the sentence we can try to achieve this by looking at the scores of this right part this is the score of a discriminator so what is discriminator classifier we generate a sample of adversarial examples adversarial sentences and we have a label that it is an adversarial sequence label 1 also we can check for label 0 for natural sentences from our sample and we can try train the classifier and we see that in many cases this classifier are not very good for our attacks it means that this discriminator even after training can distinguish adversarial examples and normal examples it means that our generative model and our generative attack works pretty well if we try to solve the classification problem but let's go to the main goal of the paper let's try to check if we can do this for adversarial attack on machine translation models and basically what we do we run the attack with different hyperparameter value that corresponds to the power of attack how much we should corrupt the output and input because it definitely is to correlate it how we change the input and the output what next we can do we try to assess the quality of what we are doing of the attack so we basically look at some similarity score for the initial sentence, the changed sentence x' after an attack and the original translation and the translation after an attack so we want this guy similarity to be large and this similarity to be small for a successful attack so basically we need to plot both of them so let's do this and look at what's going on so we try a different sort of attacks and see actually what's going on so each point here is a different hyperparameter setting different colors corresponds to different sort of attacks and basically what value we want to be, we want to be here where we have a similarity between initial bird and okay for initial sequence x and x' attack but low similarity between the translation after the attack between the wire translated and wire translated after the attack and as you can see for all metrics, despite we want to be here we have a small improvement I would say that for all classes methods we have significantly lower the diagonal which means that we slightly or slightly lower the diagonal which means that we slightly change the change in the input and the output are pretty much the same okay and also an additional experiment shows that in some times we still can be pretty successful we added another type of attacks that's called character based attacks we swapped characters and also we combine these ingredients and we can see a new growth of points here that again corresponds to the different now for the different health parameters and generally we see that we change the input less than we change the output it means that in this case we can attack machine translation models basically we think that this effect comes from the idea that we need to find some big spots of deep translation models and basically it's not an easy task and we focus on something that lies outside of a training sample of such models and swaps of characters make us to get here and to improve over the baselines so basically it means that if you want to change the classification label we still can try to find some change in the input to get a change of the label with very small perturbation of the input if you look at machine translation models the situation is different typical approaches doesn't work here and what we can do is try to go to the character level and in this case we are pretty much successful so we can say that machine translation models are pretty strong like many other sequence to sequence models that we think so that's all about my talk do you have any questions? Any questions? We don't have questions in the audience we have one question in the audience understood you right but do you check that the sentence that you change for example the review that you showed in the beginning do you check that the actual meaning of it stayed negative but the label changed? Yes I can try to show some examples from the paper and you can see that typically the change is pretty small and if we show this to a human we would say that it still would identify something like this the first example may say that this changes to something that's not very meaningful here we say that human can say it's a misprint but definitely it's a gallon but after translation we see that the translation is pretty different and the meaning is not the same for even machine translation some successful examples so you check that the meaning stays the same while the label changes actually if we are talking about the first paper we even did some human elevation and they say that in most cases the meaning is pretty same okay thank you you're welcome okay let's thank the speaker again okay thank you good everyone let's start so the prevalence of online shopping has made it the foremost method for purchasing various goods the online customer reviews play a crucial role in providing valuable insights into customers' interests and knowledge of the product but how can we discover these insights automatically and formulate them our paper user review summarization in Russian researches this question so what is user review summarization well the logical extension of text summarization is multi-document summarization where we summarize multi-document but from the multi-document summarization stems the opinion or user review summarization which uses the specifics of human opinion presentation in various sources on the internet here on the slide you can see an example of user review summarization the input consists of reviews covering different features of the entity in different colors and the summarization model should analyze given reviews and produce a summary which should cover all the reviewer's opinions so the researchers in the field explore both supervised and unsupervised settings but while supervised setting is widely used due to its effectiveness it requires golden summaries for training which can be extremely difficult and resource-intensive to produce moreover actually the majority of the existing studies focus solely on the English data and neglect non-English languages because they lack publicly accessible resources this hampers the opportunity for broadening our understanding of cross-languistic properties of our summarization and we try to change it to collect the data set in recent years the researchers suggested a lot of methods for opinion summarization some of them suggest using automatic data aggregation while others use weak supervision in the form of seed words for further major topics identification some articles suggest using information about such topics or aspects to model summary generation during our work in the project we experimented with the best performing contemporary abstractive and extractive models from the articles on the screen we employed different methods which use and aspects information to create a summary as our work focuses generally on the data and training and not on the architectures let us overview only the main ideas of these methods so the first method that we will be reviewing is plan sum it extracts sentiment and aspects distribution from the data and then fuses these distributions and token embeddings the result of the fusion are then fed into elastame decoder with attention which composes the actual summary another method is sum does not have a colorful scheme but mainly it uses multiple instance learning model from the previous works of the authors to induce aspects controllers and then feed them into the transformer model the original transformer model the controllers are actually calculated using with supervision in the form of manually collected seed words another method is quantize transformer which uses transformer encoder and decoder to quantize the sentences of the reviews to find the average opinions so as valuable aspects and formulate it with the existing using the existing sentences and the last work which we review in this project is the semantic cutting encoder which continues the work of context transformer but creates the distribution of aspects and ranks them and choose the top elements as the basis of the summary so let's talk about the data the standard data sets for opinion summarization in English language are Rotten Tomatoes Yelp, Amazon, Oposum and Space data sets each presenting data in different domains unfortunately from our knowledge there are no publicly available data sets for user review summarization in Russian and the majority of Russian-aligned services do not allow to use their data therefore the data was collected from the open internet source TripAdvisor.True with the help of verb scrapping resembling the collection of space data set we collected around 1 million reviews of hotels from 11 cities and structured the data in a convenient way hosted by one of the reviewers which also show the statistics of the collected data sets some of the data distributions but as you can see the statistic are fairly similar among the train tests and validation splits the labeling process for the validation and test splits of our data sets involve manual annotation by us the authors similar to space data sets we have summaries per split we created 28 summaries per split we wanted to compare the performance of the models trained on the collected Russian data and the existing English data sets which is why we followed the annotation of space data set the set of aspects was taken from some paper translated to Russian and fixed for manual summary construction every hotel was labeled based on 50 reviews stratified by rating to ensure that both low and high rating reviews are present in the data we manually filtered the concepts and phrases corresponding to specific aspects and chose the most repetitive of them as a summary basis we well let me that the human written summaries might provide some restrictions on the operation of models and leave this for further research we choose models with the highest metric values on space and Amazon data sets which utilize aspects information for the correct comparison on the Russian language data set during our experiments we trade abstractive and extractive methods and compare their performance some methods were not taken into consideration because they either utilize self-supervised learning apart from unsupervised learning in these methods or propose novel algorithms which not consider sentiment and aspects information for our experiments we employed the Rouge metric which is a standard metric in this area we also experimented with changing methods for processing and sit was extraction steps but as this did not lead to any improvements we wouldn't stop on this here you can see the results of different models evaluation all the models were trained on the collected data but the models with FT postfix were additionally fine tuned on the small part of translated space data set while the evaluation on the collected data set shows clear dominance of fine tuned ASSUM evaluation on the space data set shows several well performing models among them fine tuned ASSUM which show the highest biogram Rouge metric and variations of plant sum which show higher unigram and longest common sub sequence metrics so having conducted a manual analysis of the produced summaries which can be also found in the paper we found out that obstructive summaries contain more specifics from their views Extractive summaries contain more general information but on the other hand quantized transformer and semantic coating coders so as extractive summarizers included personal information in their summaries and plant sum which produces more human readable texts catches indirect relation to aspects while ASSUM describe the fixed aspects more precisely but with limited lexic variety so in this work we explore the application of modern solutions to the Russian language user review summarization we managed to collect around 1 million reviews of hotels and annotated parts of the data for evaluation we compared models from unsupervised and weekly settings the best performing models among the approaches were adapted for the Russian language analysis and trained and fatuned to summarize the opinions in the hotel domain our conclusion is that abstractive approaches outperform extractive approaches on the collected Russian dataset in contrast to findings for English data presented in the recent article proposing the semantic coating coder model so specifically the best performing model on our data is ASSUM and on the space dataset are plant sum variations further research may focus on the stylistic limitations of human written summaries for better model performance coherence and readability analysis of the summaries and ways to improve them researching the self-supervised methods which are actually excluded from the comparison as some of the well-performing models on the standard datasets so thank you very much for your attention if you have any questions you're welcome thank you for the talk I think about three slides ago you've provided quite a cool yeah this one quite a cool summary of the specifics of which models and of their performance on the reviews you've collected and I wonder is it possible to well some of these specifics clearly are but is it possible to measure all of these effects were you going to do that it's actually first of all these are the findings of the work we've done so it may differ from more general information what we found out and secondly it was measured manually as I mentioned before because the test and validation splits are actually not that big so we managed to measure it manually I see could you please remind me what was the size of you know the datasets based the side of the part of the dataset that you were making the conclusions on based on okay so we followed the annotation of space dataset which actually achieves some kind of good results they had 25 hotel summaries so for every hotel we have a review set around 100 or 200 of reviews and for each of the hotel we create a summary so they had 25 summaries for validation split and for taste split so overall 50 summaries and we had 28 summaries for both splits so 56 summaries in total I see thank you you're welcome thank you very much for your talk I was wondering do you consider now only the hotel reviews or do you plan to do more on other topics or it's just hotels now so the standard as you can see the standard datasets you can see but anyway the standard datasets review the different domains but we chose the space dataset because it stated the most clearly the procedure of collecting and annotating the reviews and it was the easiest domain to start from but of course it is a bright prospect of the research to try with different domains which were actually done in English data in different articles so yes the answer is yes thank you okay more questions we don't have questions in the audience zoom okay I guess we don't have questions on the zoom well we do have questions okay yes just a quick question thanks for the talk and as far as I understand you annotated the reviews well you wrote the summaries for the reviews based on a set of aspects taken from one of the prior works and of course this choice of the aspects influenced the result in summaries quite a lot so I do think if you choose another set of aspects your findings would be much different about this we actually explored this avenue so we tried to find the seed word extraction methods in order to comprise a different set of aspects different from the aspects from ASUM and we tried all the different word embeddings all the different methods described in different papers but actually it didn't produce any better metrics according to the summarization matrix which is why we stick to the ASUM set of aspects but if anyone could find a better way to extract aspects this could be another avenue of research. I guess it was not exactly what my question was about because you evaluate your model on the summaries which are written by humans taking into account some set of aspects so your gold standard would be different if the aspects are different right? Yes of course the production of summaries was based on the given set of aspects the set of aspects was taken from the ASUM method and they actually like provided it because they collected it manually from a great like from a great experience human written and they extracted the most valuable aspects but if we had another aspects we could also experiment with that. My question was about your expectations for example your current set of aspects includes things like cleanliness of the apartment I don't know food quality etc but if if you choose some completely different set of aspects the easiness of finding the hotel do you think that your ranking your results will still be the same? What is your expectation? I'm not sure the experiments are not just I understand it is quite difficult because the model generates the summary based on what it was trained on so I guess it can find different aspects and it would give even lower quality but better summary if that makes sense so probably Rouge for that purpose is not the best metric but the one that we used because we had the fixed set of aspects okay thanks thank you we should announce that please don't forget that we will announce the best paper during the closing session so please make sure to attend it so as to learn what is the best paper in the NOP track okay so let me just first introduce you so thank you for coming so Artem was supposed to be here but unfortunately for some personal reasons had to cancel his 3 plus minute nevertheless it's a great pleasure to me to present another very interesting speaker from Emirates today Artem I know him since 2019 and I should say that he always amazed me by his energy dedication to NOP research and dedication to the topic as well which he's going to present today over the years so I've seen how Artem emerged into a leader in this topic in this field he started a lot of active learning and certain estimation techniques and published extensively at the very top conferences and continue to do so so his record track in NOP is really impressive and today we will learn about this very important I think topic uncertainty quantification for NOP and more specifically for LLMs so without further ado Artem please go ahead Thank you very much for the kind of words I'm very apologize that I cannot be there in person because of health issues but never mind let's dive into very interesting topic I currently work on safety of NOP models and let's go ahead so we'll have some introduction and some estimation background this topic will be more focused on certain estimation and especially on some estimation for language models I will also present our last effort regarding summarizing and systemizing these diverse works on some estimation for language models in one package in one Python library and finally we will conclude with some suggestions so first and first we know that language models are very good becoming very good in multiple tasks and basically regardless of what task you'll pick up you'll find something like that on the papers with code that we approach or exceed the human baseline you can see that well most of the tasks might be considered assault or maybe most of the middle complexity tasks assault but playing performance as accuracy is not the only thing that we want to pursue that also safety and safety is related to two other aspects first of all it's bias and reliability so let's start with bias so what we usually have we have some data with some problems with maybe some garbage and maybe some biases some biases pure correlation between target variables and some feature that shouldn't be really being taken into account and we train a model and we get of course a bias model that picks up this particular dependencies and use them to do these predictions which is of course not good because well we can think about many biases for example one bias if you classify a mood a person by a picture and you pick up a color of his eyes as a feature that's probably one example for bias which you shouldn't take into account because of course the color of the eyes makes any sense for predicting the mood but due to your data you can pick it up as a viable feature and use it in the future so what we want to do is in the biasing in fairness we have like garbage data with some biases and eventually getting and then bias the good model so how we can do this one important case of bias in NLP which is usually considered is social biases and stereotypes like depending on gender, race, age and here's one example where we try to determine the profession of a person where we try to determine the occupation of a person want to determine whether whether a person will be a surgeon or a nurse and we can find out that sometimes classifiers look at the gender of the person and just put all women to nurses category and all males to the surgeon category which is completely unfair and should be avoided of course in practice because if we do this we will facilitate biases in the data in our social stereotypes so how we can deal with this well there are two techniques that we can apply to our models and techniques that reduce the importance the effect of bias in the original training data first we can reweight instances in the training data so the signal from the loss from the training loss will not bring this spurious correlation between protected attribute and the target variable this is when we try to unlearn or forget about some attributes in our future representations and here for example we have this additional loss component that help us to forget about gender race and other stuff so the model cannot predict from the future representations these protected features so eventually it becomes less biased balanced alright that's how we deal with bias now let's go to reliability okay so what is reliability and why we should care first of all let's look at this an example as you can see there's like a banking application where a person asks for his balance and well this banking application works perfectly fine when the questions are related to the topic but when a person asks something unrelated like from the sports it tries to answer his request in the way it was trained to do and it fails because it answers something which shouldn't be answered and that is an example of out of distribution question where a person asks something from the domain which is not part of the training domain of the model so instead you're probably saying that okay sorry I can answer your question because it's not my type of concern not of my type of domain knowledge so alright so that's one of the example of out of distribution input another example well a little bit more critical safe critical application diagnosis in medicine where we have for example some symptoms or maybe anamnesis of a person and your model has to make a diagnosis might be affected the treatment of a person of a patient in the future and of course there might be some out of domain questions that they should try to avoid try to ask a person for the help but it also can be some situations where we have something like very close diseases like SARS and COVID and it's very difficult to distinguish between this particular amount of information you have we probably should try to ask for a physician or a real person another more complex system to make a decision for that instead of making a diagnosis that probably can harm a patient in the future so that's two examples let's go ahead so what we have reliability we have to always remember that model capabilities are very limited because due to limited amount of data it was trained on there's always training data set and it's limited and going beyond this data set always has a risk of making model answers completely unreliable and it's also a situation when our task has ambiguities like in SARS and COVID where it's very difficult to distinguish these two diseases with lack of information like lack of features so this second thing ambiguity in the task is another concern we have to spot these areas of ambiguity and also try to abstain from making any decisions in these so what we need to do to achieve abilities to develop mechanisms that take into account these limitations when the model is deployed and reliability is basically a capability of our system to apply this mechanism in a perfect way to detect ambiguity and many other stuff like maybe adversarial attack detection so how to handle reliability how to make your model reliable this slide is a little bit marketing here because basically we're promoting here some of our work which was published in ACL in 2022 and 2023 and other conferences actually there are some solutions to reliability problem in terms of classification and how to detection the last one will be published in ACL this year it is an intersection between devising and uncertainty but of course there will be a lot of there are many more works in NLP and uncertainty for NLP and we will discuss them later just a small slide here about our work okay so our new concerns what we have today today we have a generative AI era and in generative AI era models can generate something inappropriate something that shouldn't be generated and in particular case with charge APT here this is a case where it gives an incorrect answer here we can see that well we're asking how many letters in the word 19 and it's incorrect then we ask when we try confused model even more and it becomes even more confused so yeah this happens all the time with language models and I should check this example for this lecture and well the first one was fixed but the second one is not fixed it's still confused model into thinking that there are like any number of letters in the word 19 another example I apologize before Alex Panchenko for that but this is a question about his biography like tell me about Alex Panchenko and the model also hallucinates some facts about his biography and very confidently say that he's a professor I mean like he will be probably in the near future right now he's not a full professor in Hamburg but it's pretty convincing in saying that saying some false facts about Alex and that happens all the time with this kind of models so what we have what concerns we have in the area of generative AI so models can generate unacceptable output and we should first consider the hallucinations like the false facts that they generate we also should consider something like toxicity because you know the data model can be trained on can have some leaks of toxic toxic texts and we should avoid that we also should care about truthfulness because sometimes model cannot do the task and it should be quite explicit about that I cannot do this it should be truthful and also one thing which is commonly put into consideration is personality that the model should not pretend somebody else it should be explicit about it's personality that it is model it is like like assistant it's not some sort of like a human professor etc giving you this answer so how to deal with this problems in general there are a few ways to do this already and people are applying currently to all this language models gender GPT and others it's reinforcement learning of course where models is trained on a big amount of scores of assessors to and the scores are essentially aimed to reduce bias reduce success to reduce many other issues with the model there are other approaches to this problem like contrastive learning you can also just fine-tune on some good answers that people craft by themselves also you can do some filtering of the input data or output results with a stop-list another stuff one of the colleagues that were visiting the embassy I was from my tutoring and said stop-list is a way to go with the language models very good thing it works much better than anything else fact-checking and another stream of work where people try to look at the output of the model and do some you know fact-check checking with the databases and finally which is I think one of the most important interesting for me is uncertainty estimation can we apply some decimation in for language models right first of all let's just go through as an estimation background I hope pretty fast again let's consider one small example here consider we have a classifier that is going to distinguish cats and dogs and it works perfectly when has perfect picture of cat and perfect picture for dog it works good it predicts what it was intended to do but what we have here is something weird which you know cannot be classified into a cat or dog class here probably in model should output something around 0.5 something 0.49 or 0.459 to demonstrate that it's something weird it's something super uncertain and maybe we should call it person or somebody else to work with this image but unfortunately like this probability scores that obtained through softmax classifiers are not very good the reason is here the softmax probability it gives you it can be used as a estimation score but unfortunately it gives you uncertainty like this so in most of the area you are completely certain here and here and you only uncertain in this small decision area between two classes and that's what we get from softmax classifier but this of our example with the cat and dog this cat and dog image can appear like anywhere it might appear something like here and of course in this areas we will be completely certain and we will predict particular class so overconfidence is the problem of softmax probability and certain estimation so what we really want to have is something like on the right picture where everywhere we don't have any particular evidence and we don't have any data we are uncertain and also we are uncertain in the area between two classes that's what we want to have alright so that's why we cannot use softmax probabilities well we can but sometimes it doesn't really what we want to okay so what is uncertainty in particular well there is no unified way of specifying certainty scores it can be anything which works very well for us for our task like audit detection or selective classification it might be some sort of distances probabilities entropy of course or estimation of an error but while in biasing theory there is a particular way of defining uncertainty and this is basically entropy of a probability distribution and so we have a trained data and we can well we can say that uncertainty is basically an entropy of predictive distribution and this distribution can be parameterized with a neural network and of course if we are biasing we probably like to put some sort of like prior on our parameters so they have also some sort of distribution and then if we then we can rewrite our predictive distribution in this way so we have this formula which we can put it into the predictive entropy formula and get something like this but of course I mean we could do this in a biasing way we could try to try to estimate everything correctly using some sort of approach from the biasing theory we can do some variational inference etc but usually we don't have an access to we don't have a way to do this usually the biasing neural networks is very hard to train and they are usually very difficult they also have some drawbacks like storing additional weights and another stuff so let's go back to types of uncertainty and look at our classifier again here we have a situation where we have a lot of information on the data and we have a classifier tries to distinguish two classes but unfortunately in this area, small area between classes, these classes overlap this example of high aleatory uncertainty where we have noise in the data where we cannot easily distinguish between two classes just simply by drawing a line so in this situation we are saying that we have high aleatory uncertainty which is related to noise and ambiguity in the data another situation where we have lack of data and we have some sort of freedom to draw this linear classifier in various ways and it will perfectly you know, distinguish two classes but eventually the situation like here is ambiguous for us because again this might be a trained set but in the real situation that it can be located here in this data can be attributed to one of these classes so in this case we say that we have high epistemic uncertainty which is related to lack of knowledge so we don't have enough information enough data to draw this classification boundary all right so by definition the whole predictive uncertainty is basically a sum of epistemic and aleatory uncertainty and then what we can do is to write some I would say from the previous formulas we can derive several formulas for epistemic uncertainty and polarity uncertainty first of all let's look at epistemic uncertainty epistemic uncertainty is basically as I said it's a lack of information so what we here define epistemic uncertainty as information that we get about model parameters after we see the target variable y for the object x so this is how like our knowledge about model parameters change after we get information about this particular instance so if we write this formula a little bit we eventually get this formula where we subtract our predictive uncertainty from this term which is basically an expectation of entropy for each particular realization of model parameters all right and okay so what is aleatory uncertainty then well by definition we have a predictive uncertainty the sum of epistemic uncertainty and aleatory uncertainty so simply we can derive a formula for aleatory uncertainty here so this is basically this expectation of entropy of predictive distribution for each realization of model parameters so again in bison theory we have like perfect formulas for these things but in reality we cannot you know estimate them directly it's quite difficult where there are several ways to do that in bison modeling of course like bison backprop variation for instance etc etc but usually people don't do this because it's a little bit more complicated and a little bit more overhead to do that and it's very hard to train bison models right now again let's look at this formula so predictive uncertainty is a sum of epistemic and aleatory uncertainty we can see that epistemic uncertainty is a mutual information between model parameters and target variable and aleatory uncertainty is an expectation of this entropy of each realization of model parameters and again let's look at how like a certain just look on the charts we have some sort of like two moons dataset aleatory uncertainty basically this line between two classes, this is a decision boundary epistemic uncertainty is basically all area around this training dataset and then we sum these two uncertainties together so we get this predictive uncertainty so we have both certain areas in the like in the area without training data and between these two classes where it's about where we apply different types of uncertainties aleatory uncertainty usually can be used for like clean data we can spot some noise in the datasets like epistemic uncertainty here can be used to detect outliers of course it can be used for undesirable detection and there's also a possibility to use it as as a criteria for active learning for selection criteria in active learning total uncertainty is used for selective classification and that is kind of more the most critical thing for safe critical applications where we need to abstain from making a decision somewhere for example in medical applications all right let's just have a very broad overview of methods for incentive estimation we'll get to in particular realizations of these methods where we discuss particular methods for language models so in general a very strong approach to incentive estimation is basically using ensemble of models and look at the diversity of predictions of these ensembles of these ensemble elements so if while the diversity is high the uncertainty is high so the only thing you can do with a Monte Carlo dropout Monte Carlo dropout is a technique where you make not one prediction but multiple predictions but you enable your dropout layer so each prediction becomes a little bit different from one another and you can assume it as a cheaper version of ensemble you don't need to train multiple models but you just have like one model but you apply different dropout masks a little bit different predictions and again like we are looking at diversity of predictions if diversity is high then uncertainty is high there's also density based methods where we try to approximate our training distribution with some with some models based on the Latin feature representation so we just try to create a training data distribution and analyze whether our instance belongs to this training distribution or not and finally there are some techniques related to training loss regularization training loss regularization well you add a regularizer to the loss it helps you to calibrate your model a little bit sometimes it's also going to be helpful in selective classification okay so why is uncertainty estimation for language models is hard well first of all we have not one but multiple prediction predictions in a sequence these decisions are based on virus sampling algorithms we also can get the final sequence not just like simply looking at the maximum probability token but have a multiple beams and select the beam in the end and of course we should remember that predictions of language models are can is hard to normalize them because again each like a sequence well you can create infinite number of possible sequences and how to estimate the probability of each of each of this sequence and how to normalize them so this is basically a very hot topic for hot ongoing research topic right now okay finally we get to the standard estimation for language models let's look at what we have again the simplest techniques are very similar to what we have for like for the standard classification models we can look for example on maximum sequence probability here is basically we can estimate the probability of sequence by just multiplying probabilities of each decision in a sequence and you know subtracted and get our uncertainty score there's also like a very common thing is to have an average of log probabilities of our tokens well sometimes it's called perplexity, sometimes it's called normalize log probability again it depends on whether you do it with explanation or not and also you can simply just you know calculate entropy for each particular token and average it and there are many other you can figure out or you can think about many other ways of aggregating individual predictions of individual token but these are just the most common ones in the literature okay a little bit more complicated stuff I mean not the complicated stuff but a little bit more interesting stuff it's point-wise mutual information here for example it's a way of addressing it's a way of addressing that some sometimes model outputs generic stuff and basically maybe uncertainty of this generic stuff is not that crucial so I mean like if it outputs something very generic maybe we shouldn't be that much of a concern about that so there's a approach to correct things with this additional term where we simply run the model with some query and run the model without query and so we look at how the model how the sequence what is the probability of sequence without any particular input how generic it is a little bit more complicated way of doing this is conditional point-wise additional information this is an idea where we basically fall back to the simple perplexity but sometimes where we are very uncertain we look at the probability of the generic sequence so this basically it's so most of the time we using this term but sometimes if this entropy is high enough we also add this term with the generic it's a little bit faster than this one and words also a little bit better okay assembling I will not spend all of time on assembling you are unfortunately compared to like classification assembling in generation where it's not that good I mean classification is one of very reliable algorithms assembling usually if assemble doesn't give you any improvements probably nothing will do but in sequence generation ensembles according to our experiments do not perform that well anyways so for assembly we can do same stuff as before we can just look at the average probability from picture of the ensemble element we also can look at the average probability of course and there's also more stronger technique which was suggested by Malin and Gels is a reverse mutual information where we look at the logarithms also of the each realization of of each individual ensemble element we also could use some sort of like Monte Carlo dropout here Monte Carlo dropout in sequence generation works a little bit worse again and note that you probably need to keep the same dropout masks across multiple sequence generations to keep your scores good enough yeah all right so density based methods these are quite strong methods especially for out of distribution detection in here are two papers that propose this for for order detection in sequence duration tasks these are this our work which was published on ACL in 2023 and very concurrent work which was published on iClear in 2023 also basically the idea of using the density base techniques in sequence duration it's basically the same stuff in classification we use so-called mathematical distance as a certain estimation metric a certain estimation score and you compute it like this so the idea behind that is that you estimate the probability of you estimate the conditional probability of your particular instance belonging to some sort of class in your training data like for example here we are looking at the probability of x belonging to yellow class and we say that well this probability is basically normal distribution with a centroid with a parameter m in the center of this class with the covariance matrix sigma which might be computed just looking at this particular class or the whole data set this just different ways of computing the whole distance so if you do the directions correctly you will eventually get this distance score so uncertainty will be basically the distance between the centroid of your class to your data point where which you are trying to estimate uncertainty for and the higher this distance is the more uncertain you are so because your point is lying far away from the training data and well of course in sequence generation tasks you don't have particular classes so basically in sequence generation we have only one single class where you have all the training data in one of the single class you compute this centroid you can compute the covariance matrix and then you can estimate the distances between centroid and your instance and that is the question okay so how do we so what is H here H is a representation of our instances and in sequence to sequence models we have two options here we have encoder and decoder and we test both of them and they work pretty well and you can take the representations of each of the we have an embedding of the input question input query and you also can do the same thing with the output you can get the embeddings of all output tokens and also average them and get this embedding of the output sequence so then we can compute this we can compute the covariance matrix we can compute the centroid of course using our training data and determine whether we whether this instance is out of distribution or not pretty simple there are two modifications of that one is based on distance plus pca plus minimal covariance determinant pca helps us to reduce the dimensionality of our representations and reduce the effect of outliers the same thing goes with minimal covariance this determinant helps to filter out some noise when we compute the matrix and okay there is another option where we calculate the distance for our particular point and we subtract the distance to the some sort of like global central centroid of the background collection in this case we have a big background collection like maybe C4 data set we calculate the centroid for the C4 data set and look how close our instance is and this idea is just like that we don't want to be high and certain for very common very common sequences to very common queries to very common outputs so we are in this case we are uncertain only when we are both far away from the training data and far away from like the pre-training data set okay all right semantic entropy this is one okay so let me reiterate with density based methods these methods are very good for audit detection and there are type of specific and certain estimation techniques so if you have even especially sequence to sequence models they are showing pretty good at tasks for machine translation summarization question answering to find some experiments in our paper in the paper of our colleagues in the concurrent work all right semantic entropy is more interesting stuff you know probably that model can generate multiple similar sequences in terms of meaning but very different in terms of surface realizations like you can ask who is the president of United States and for example like model can answer like George Bush or George Bush is the United States president etc etc this will be essentially the same thing and we want to take into account the similarities and semantic entropy does exactly that it's samples several predictions from the language model like A1 to AM then it clusters into meanings like meaning clusters and then it estimates a certain entropy on top of these meanings not the particular sequences so and each meaning can contain multiple generations like again regarding this president they all basically can be put into one single cluster to calculate the probability for each meaning the author just simply is some probabilities for each individual sequence and then basically calculate the standard entropy formula on top of the probabilities of these meanings well this works pretty well but surprisingly that black box methods work even better in semantic entropy we need an access to lockets and probabilities of the output language models but sometimes or even actually usually some models like charge gpt, gpt4 and many other APIs shouldn't provide you an access neither to embeddings nor to lockets of the models and then you probably need to only only you can what you can do only is to analyze its outputs so what you do here you again sample multiple outputs from the language model then you can calculate get bearable similarity between these responses and then you can compute and sort of set the estimation on metric on top of that first what these authors of this paper tried they tried multiple similarity parallel similarity scores one of them is basically jacar similarity where they simply look at the bag of words of two answers surprisingly it worked and another thing which is more elaborated is to use of the shelf and lie the Burton model for an LP entailment so the idea is just to look at two outputs and analyze them with the Burton and determine whether one is entailment of another so if one is basically entailment of another another is entailment of the first one you say that they are essentially the same thing so they are similar so this is the second way of computing similarity which is more effective so now let's get to uncertainty scores again surprisingly very simple score uncertainty score here is a number of semantic sets here you again you calculate this pair of similarities you basically cluster them into semantic clusters and calculate the number of these clusters and if the number is high then you are uncertainty if the number is clusters is low then you are certain that's basically it again this works but there are some better versions of that the second version is to compute doing the spectral clustering on top of these similarities so instead of like just trying to create this hard adjacency matrix you create like I would say salt adjacency matrix where you put you have an adjacency matrix with scores of similarity of which output sequence to each other and then you can do some sort of spectral clustering where you can basically look at the number of these spectral clusters so essentially this method does this another more simpler method but actually from our experience a little bit more effective is basically looking at average pair of wise distance again we can construct this matrix and we can just look at some of pairwise similarities between output sequences and then we can just average that and get the uncertainty score so again if the average pairwise distance is high then we are uncertain if average pairwise distance is high then we are certain yeah so these two things work actually pretty pretty good so and moreover I want to again emphasize that for this particular case you don't need anything you don't need an access to the model you can just do this you can apply this method to charge a bit for anything that has an API finally the method is called p2 from the antrophic paper which basically says well why not we can ask the model directly and they did they just did this stuff they they related the question multiple choice question answering they provided the question and the answer of the model and then ask it whether it's true or not surprisingly that the model as you can see here it's answers to the second question like whether it's true or not can be used as a proxymetric for uncertainty for its original answer as you can see that for for the wrong answers the model is usually uncertain like the scores are below 0.5 closer to 0.4 and for the right answers it's pretty confident it's it has this distribution as you can see so this interesting way of looking at and certainly like again asking the model directly itself it's probably also interesting direction for the future work but again I want to emphasize that this despite it works in this particular case in work this researchers in our experiments and experiments of other people that didn't work really well for example machine translation of other stuff the question answering also sometimes it doesn't really work alright so let's do some sort of summary what works and what doesn't for audit detection I was just going with the density based methods of course and I was just considering robust density estimation first for second classification I would look at the black box method its average pairwise distance and lexical similarity also yeah and also probably it's a good way to combine density based methods and perplexity that was shown in one work on iClear work that it helps we also get experiments where it helps but in text classification current this P2 method sometimes doesn't work and symbols also sometimes it doesn't work right now let's get to the final part of this talk to our program framework LMPoligraph so it basically helps you to know what LMS do not tell you so LMPoligraph is basically a Python library which accumulates a state-of-the-art instant estimation techniques it supports state-of-the-art models, state-of-the-art models, GPT-like models it has wrappers for API you can just use a very small number of just a few lines of code to add uncertainty to your language model it also provides a benchmark to label methods instant estimation techniques maybe on your data etc. we also will provide a live demo I hope soon maybe on MLP maybe on AAAI alright, some demonstration examples with LMPoligraph in our demo, for example here we are asking model to translate non-existent language like translate into wasabi and language and you can see that model is completely uncertain because of our instant estimation metric is zero and for French it's pretty easy as you can see model is completely certain about its output another example with with the knowledge of the model as we can see the model doesn't know Russian songs from Russian artists from Russian singers but it knows pretty well that the songs of British of Beatles and as you can see it tries to in Russian case it tries to imagine something like it tries to predict something like Irina Allegrova Allegrova, Allegrova tries to predict something similar but essentially it fails and now we can detect that it fails another example is like asking some simple and complex questions, if we ask a complex question like how we can cure dinosaur, consider like dinosaurs came from Earth, we can cure them there from Pumania and despite it provides some you know, a list of suggestions about how to cure Pumania for dinosaur we can see that it's completely uncertain but when we ask the same thing for the human well, it shows pretty decent confidence the same thing with like how to perform a kidney surgery well, and surprisingly the model gives you a pretty good plan for a kidney surgery to perform with high confidence but if we ask how to perform a kidney surgery with only one arm now the model is completely uncertain about that yeah, also doing a kidney surgery with one arm is not very good but you see that a certain estimation scores works here that it shows that this is unreliable answer so finally some words about our team there are I want to acknowledge our great team for making this library for developing stuff technology Maxime Panov who is at the conference who is also a part of this initiative here many authors they are from different organizations there are many others but these are the main organizations of our work so in conclusion let's look at some takeaways okay, so some things that we should consider beyond just accuracy beyond just performance metrics these are devising fairness and reliability so this estimation is a crucial component of machine learning systems including language models and for all the considered density based methods like robust density estimation for selective duration try black box methods because they work very well and maybe you can also try combination of density based methods and complexity well overall don't trust LLMS try LLMPOLYGRAPH to reveal what LLMS do not tell you I want to also note that we have a very strong team at MBUI that work on devising fairness which is led by professor Tim Baldwin and I have an over and over to be one of his colleagues in his group regarding fact checking we have also a very strong team professor Praslav Narkov and I'm working also both in the direction and also in uncertain estimation also I would note that Maxim Panof is also one of the colleagues who is working on uncertain estimation right now alright, so that's it thank you very much for your patience like if you have any questions I'll be happy to answer again here's my contact again here's our GitHub link please give us some feedback it's still in better alpha maybe version but anyways let's see how it goes Artem, thank you very much for this most insightful talk so first of all I would like to ask if there is any questions from audience if yes you can come here or go to the mic thank you Artem for the very nice talk okay so my question is just maybe clarifying some things maybe I'm not understanding have you tried to analyze how the uncertainty of the model maybe correlated with error rate or the wrong decisions that made by the model does that understand it's a different thing right? well actually this is a task of selective classification where we want to all uncertain instances to be incorrect and all certain instances to be correct so we want to sort our data set in this way so all very uncertain instances have more errors and all instances that have correct answers are confident so when we maybe do some abstention like 10% we abstain from making prediction for 10% and give these instances to human expert or another system we get a better boost we get a better outcome out of that so yeah we did this we have a couple of paper regarding test classification and we also have a paper regarding selective generation where for example we solve the question answering task with multiple choice question answering so we yes we analyze by looking at how good for example it in multiple choice question answering which answers are certain which are not okay thank you and a short question about just a funny question have you tried this lempligraph to real humans what they generate and is it possible to maybe to analyze what humans generate well unfortunately we cannot ask a human and asking to write the answer for the same question multiple times and it's it's polygraph for language models it's not for human it's about how we can analyze the distribution of predictions of language model not a human and fortunately for human we have the common polygraph right Artem I would not agree with you because if you're ever making sociological tests you know like in companies this is what they do they ask you again and again the same question and actually they kind of sample and see whether they so actually that works but maybe I haven't tried that how frequent human are certain how frequent a lemon are certain yeah they have like a hundred questions but then it's just repeating more or less the same question different paraphrasing and then they basically just check whether you answer the same way so actually that's not that far from how they do it okay more questions from audience okay Artem maybe let me ask you this question like really the fact that assembling doesn't work in BlackRock works so well is what has been empirically discovered but do you have some generic explanation or some insight why is this so for real lamps and why it works so differently from some other machine learning setups well maybe we haven't we haven't designed ensembles very well I mean there are several ways to design ensembles we can just train them on different data sets we can apply multiple seeds to them but essentially if they are pre-trained they are very very similar so that's problem so they're very very similar and maybe if we look at ensemble of multiple different models like for example if or we apply some techniques for building ensemble like in LM Blender for example that will help us to help us a little bit so I think we need to add more diversity to ensemble in this case I think that this is a answer but maybe there are some other issues maybe there are some other kivits in ensembling so I'm sending your answer correctly that the variation in let's say a function of decoder mechanism currently typical decoder mechanism is like less than variation in ensembles of some classifiers as run through Monte Carlo dropout something like this well I mean in simple text classification models you can build an ensemble just by using different random seeds right and they will be different enough in language modeling pre-training is probably even you pre-training models even harder I mean like that pre-trained on bigger data set so if you send them on small data set probably they will not be that different from each other that's a problem so the more data is in pre-training the more similar will be the ensemble elements in your ensemble and the more similar answers they will give you I think this is one of the possible answers to this question thank you any more follow-up questions from audience maybe let me ask one last but short question so you presented this approach when you just asked which model the answer is correct that's interesting but whether people tried some kind of chain of thought elaboration of this idea let's say you answer multiple times and then you kind of a sample several responses or you repeat again the question about certainty or correctness in different ways not just once this way obtaining also some kind of a sampling but in this dialogue way style yeah I think that the way the reflexive power of language models is very strong too and I saw a work where of course the chain of thought was used to improve the quality of the answer of course we know the chain of thought improve the quality of the answer or the chain of well when the model assesses its answer by itself and it says that it's not good an answer is also being used in some works I think there was a paper just a few days ago I saw this idea I thought okay well it's pretty fast how people you know get to this idea unfortunately I don't remember the title exactly but the idea was essentially similar the query multiple times they assessed the output by the model and then try to correct it multiple times really great we live in an interesting age and if you're interested in this research get in touch with Artem and for your research you might get involved as well as you see there is a lot of improvement okay so we are very late out of time now unfortunately Artem thank you very much for your insightful talk and now we need the gears for the next speaker now we switch into the gears and we have a next speaker Dr. Mohammed Malik so a postdoc fellow at the high school of economics in Moscow now at computer science faculty and he's also a former assistant professor at Islamabad in Pakistan he has an extensive research experience with both teaching and research contributions spanning almost 20 years and today he will be speaking about fragile content and target identification in low resource languages and that's another NOP talk today Hamid without further ado please okay thank you so much good afternoon everybody and thank you so much for organizing committee for providing me this opportunity to deliver a talk on this social media mining topic and to share my findings with you so let me introduce the title of the talk that is threatening content and target identification in low resource languages using NLP techniques that is the outline of my talk in the first start I want to introduce some terminologies related to this domain then the challenges and then after that problem definition related to my contribution in this domain regarding Urdu language I will discuss a case study with the results and then I will conclude my talk by summarizing the findings and then future prospects okay so coming to the first point that is what is hate speech in literature there are several definitions of hate speech exist because researchers try to define the hate speech according to their understanding their knowledge their vocabulary and their thinking prospective so these are these are few hate speech definitions I added and the references are also added you can see these definitions slightly vary according to their own thinking prospective and their understanding but let me introduce you the most common and most you can say the majority of the researchers have a consensus on this definition of hate speech that is it is a toxic speech attack on a person's individuality and likely to result in violence one when targeted against groups based on specific grounds like ethnicity religion, race, place of birth personal background language, residence cost, community and etc so when there is a hate speech there is some target because hate speech always targets someone so this is the basic the basic conclusion which we can conclude by understanding this definition of hate speech the next point which I want to describe is what are the various forms of hate speech how hate speech can be can be delivered, how hate speech can be formulated there are few common few famous terminologies presented here like cyberbullying can be used to define the hate speech, flaming profanity abusive language, discrimination and toxic comments so these are few forms of hate speech let me share with you the definitions of these forms and then we can also distinguish between their definitions and the general hate speech what is the difference between them for example if I consider the abusive language as you can see the term abusive language seeks to diminish or humiliate some person or group and when we see the hate speech hate speech is the type of abusive language so abusive language could be the parent of hate speech means hate speech is at least we can say that hate speech is at least abusive language and in the other way we can say that the abusive language is a form of hate speech similarly toxic language, if we consider toxic language so the toxic language as far as its definition is concerned the toxic language is conveying content that is disrespectful, abusive unpleasant and harmful and when we see the definition of hate speech not all toxic comments contain hate speech means toxic comments could be general without targeting anyone we can analyze and we can understand this concept of toxic language that some people have a habit of using toxic style of using language usually they use some words which are not directly targeting someone but they have a habit of using these words so that is a type of toxic language that is without hate speech in hate speech when there is a when we talk about the hate speech there must be a target okay so considering the next point that is the famous 12 languages landscape presented here that is shared by the Washington Post in 2022 the landscape describe the proportion of speakers related to that particular language here we can see the Chinese language which have a 1.39 billion speakers worldwide including all dialects of the Chinese language means they are all script some languages have more than one script similarly Urdu language also have more than one script Arabic Hindi language also as far as Urdu and Hindi languages concern we can see the population people globally that 588 million people are basically using Hindi and Urdu language for their communication as far as Arabic language is concerned the statistics are there the the proportion of speakers for Bengali language and for Russian language is almost equal and we can see that the Italian language has lowest number of speakers according to this landscape here Hindi, Urdu, Chinese Arabic Bengali Russian Italian, Portuguese German Japanese these are low resource languages so coming to the next point what are the challenges with these low resource languages while designing some identification some detection system dealing with these low resource languages the first challenge that is the very basic challenge is the lack of annotated data set we have to crawl the information and clean the information and then we have to go for a notation process this is the basic and the first challenge the other the next challenge is for some low resource languages like for Urdu, for Arabic even for Russian and Bengali language which I know because I work on these languages there are some essential resources accurate text processing tool kits are not available or sometimes they are missing as compared to other high resource languages for example for English so that is another challenge related to the resources and preprocessing tool kits the third challenge is some languages use multiple scripts for example Urdu use two scripts people use either Arabic style that is also called nostalgic style or Roman style Roman Urdu or Arabic Urdu similarly for Arabic language there are also two style in the language there are also two style but for English there is only one style in Roman style social media user usually use multiple scripts for example if I consider if I talk about the Urdu language the user usually use both scripts while sharing their opinion they share they use Arabic style and Roman style at the same time so that is the problem of code mixing that is another issue another challenge with low resource languages and the last challenge which I interested here these are not all challenges there are also other challenges but I listed few to describe the challenges to highlight the challenges related to low resource languages the pertinent famous we can say the the latest language models that are already available not every model sport the low resource languages so that is another challenge issue coming to the problem definition which I want to the problem and the task on which I want to deliver a talk and want to discuss the proposed methodology and then the results and the finding that problem definition is given on the left side of the presentation here we can see on the top of user language then hate speech that is general hate speech so that general hate speech could be categorized into threatening content could be categorized into categorized into violence incitation and other categories so today I am discussing the type of general hate speech that is threatening content identification in the nest elix script or Arabic style so on the right side the hierarchical classification of the problem is provided that the tweet or the comment of social media is going to categorize going to be categorized into two two labels that one is threatening and the other is non-threatening then the threatening contents are further considered for targeted identification that either individual is being victimized targeted in that in the threatening post or threatening tweet or the group the difference between the individual and group class label or we can say the category is that when a individual person is being addressed then we say that it is related to the individual class when more than one person are being targeted we categorize it into the group class label well my contribution handling the only Urdu language not Russian because I am also working on Russian language another low resort language so for Urdu language I have these contributions first I have a contribution in 2022 related to the offensive content identification in Urdu one paper was already published and the next paper is in the right of state then hate speech and targeted community detection that is related to the community today I am just going to describe the framework related to the journal journal target that is individual or group but in this hate speech and targeted community detection concept topic we are going to basically target the community that either it is a religious community political community it is a media community it is a judiciary community it is a army a uniform person community that who are being targeted in the hate speech then threatening content and target identification is today topic and also I have a contribution in multilingual a model for threatening text identification in English and Urdu language so today I want to share the design of the methodology related to the Urdu language concerning the topic of threatening content and target identification so here the question arises that why there is a need to design a Urdu identification system for Urdu language because Urdu is a national language of Pakistan and as if we consider the Asian region then around 170 million people are basically speaking and expressing their opinions and views in Urdu language on social media then at the same time if we consider the global perspective then around 300 million people are there related to the Urdu language those are speakers and Urdu language is not only being spoken in Asia in the southern part of the Asia but also in the USA in the UK in the Canada region so that's why there is a need to design identification framework related to threatening content and target identification in Urdu language on the right side I have added the alphabets these are the 39 alphabets used in Urdu language to design the words and sentences to describe opinion or concept so Urdu language is more similar to Arabic language and Persian language as compared to any other language what was the objective which was address while designing the framework for Urdu language the first one is to design an automated identification system for Twitter data and to classify them as a threatening versus non-threatening accurately because for threatening tweets design an effective framework to identify the type of target that either individual is being targeted or a group is being targeted the third objective was that was the preliminary requirement which we assumed which we set to start the design that the proposed framework should be based on the automated feature generation technique in contrast instead of hand-crafted feature so it should be automated feature generation technique it should be based on now coming to the proposed methodology here block diagram is added we can see that the tweets were collected from the Pakistani Twitter account because the users of Pakistani Twitter account Twitter users were considered because in Pakistan Urdu is the national language we have we have a chance of a very big corpus we can design a big corpus of data so after that the pre-processing techniques I will describe these steps individually in next slide here I want to give an overview then the pre-processing techniques were applied then feature extractions here we can see the different types of feature extraction techniques were applied like word and ground character and ground semantic techniques word and building technique, FOSTEX topic model later on semantic analysis and language model were also applied after that the machine learning and fine-tuning process was followed by Urdu Roberto it's a mistake here so at the first level the contents were classified into threatening versus not threatening then at the second level the threatening contents were considered for targeted ratification into individual or group before applying the actual proposed methodology we should have an annotated dataset that is the first challenge which I describe while discussing the challenges related to the low resource languages so annotated datasets always have a problem with low resource languages so we crawl the data hello is there any question so we crawl relevant information, relevant tweets and then process those tweets then after that we annotate the datasets according to the designed annotated guidelines before starting the crawling of the data in NLP we want to crawl a data related to specific topics specific specific research area we need to design a lexicon because without this lexicon without this seed word lexicon we could not crawl the relevant data because if we crawl all the data then how we can we can choose and identify that this data is related to our domain our topic and this is not related to our domain or topic so designing the lexicon we need to design this lexicon manually by looking by looking the type of contents for which we are going to crawl the corpus so this lexicon will also alternatively we can say that it's a seed word lexicon so we also designed 250 keywords lexicon keywords so that we can we can easily crawl the relevant tweets from the twitter urdu twitter account here I have added the examples of few keywords you can see the urdu part of the keywords and their translated part unigram biogram and trigram and also some keywords in foreground so after designing this lexicon then everybody who are going to crawl the data is in a position to get the relevant comments post or whatever the face whatever the social media platform they are going to crawl the information so the next point is the time range for which time we should we should consider the data crawling because it depends upon the situation for threatening content and targeted notification we consider the time period of 24 months ranging from August 2022 August 2022 because in this time period the political situation in Pakistan was very unstable so people were very aggressive sometimes had worried they expressed their opinion on twitter, on facebook and then we have an opportunity in this time period to get the relevant content so that we can design a better annotated data set so by applying this lexicon and the time frame we crawl the data for both for both types of content threatening versus threatening then after that we applied the process of cleaning because the data should be clean and before giving them to the annotators if the data has inconsistency that annotators will have a problem while they annotate the data set so there are few steps invested here for related to the cleaning removal of empty tweets duplicate tweets and those tweets that contain other language words for example if a tweet contains the Arabic word or sometimes Hindi word sometimes the Bengali word so it is not possible manually to translate those words into the Urdu language because it takes manual effort so we remove those tweets after that we have a clean data set then we design the annotated guidelines for annotators so that they can easily annotate the data set and for this annotation we hire the services of Pakistani annotators because in Pakistan in Pakistan Urdu is a national language and people have more advantage compared to other countries so the basic criteria for choosing the annotators was the native there should be native Urdu speakers and they should have at least most level education and have a prayer experience of annotating Urdu data because the annotation is not only for one level there are two level of annotation first they have to categorize it into threatening non-threatening and then in the second level the threatening contents will be considered for individual or group category so after annotation we have data we compile the data and compute the inter annotator agreement that is more than 80 exactly that is 83 percent here we here I added few samples of the annotated data set we can see the first second are the threatening tweets and on the second level the individual or group is being targeted in this column the Urdu part is mentioned and in the beside column is the translated version is mentioned if you see the third and fourth tweet it is obvious and it is clear that for non-threatening tweets we do not need to consider the second level because we are interested for threatening and then for threatening who is being threatened individual or group so that's why here this level of annotation is not applied but here the fourth tweet you can see that that is not threatening but that is abusive or toxic content because it's a the person is being referred with the dog tail so it's not threatening it's not exactly threatening but it is abusive the person is being abused being insulted being disrespected as I described earlier the clinic process was adopted before annotation then after annotation when we have a final dataset we applied the process of preprocessing here the main difficulty was with the stop words list because with Urdu the stop words are very difficult and very different not in English like because in Urdu we have if you see the style of writing Urdu then for one type of stop word there could be many options of stop word for example the word here is Ka it is a stop word it could be K there could be multiple versions of this stop word so that that was a main obstacle hurdle with the stop words identification and removal but we designed there were already some stop words were available because researchers did some work but we compile a big lexicon and we shade it then at the same time with the stop word removal not for transformer model for other feature engineering models we also transform the emojis and emoticons which are present in the tweets so that the context should be same if we remove the emojis and emoticons from the tweets then the context could be broken so we adopt this option that we should translate these emojis into the corresponding text and the other preprocessing techniques these are already obvious and people are familiar with them that the other irrelevant information should be removed here the demonstration of the preprocessing techniques are added first punctuation removal with the urdu text and then after removal you can see and here the translated tweet is also added for stop word this is really interesting you can see that when stop word remove the sentence the behavior of the sentence and how we pronounce how we read this sentence after stop word removal and replacement of emojis the example is also demonstrated that in this tweet the person is angry so we replace the corresponding text and then after that the hashtag and the other irrelevant information are also removed I also added in this slide some sample stop words so that we can get idea of what type of stop words are being used in urdu I added their corresponding translation also you can see these are few stop words used in urdu language here the word count and cloud representation is presented you can get the most dominating words keywords that are being used to threaten somebody now let me introduce you about the proposed methodology related to the feature engineering and then the machine learning part of the framework we search the latest and the already pre-trained language model that are available in urdu so after doing the exhaustive search we found only two transformer models that are pre-trained in urdu one is a urdu robot that the other is bird simul so we applied both models for feature engineering but I added only this urdu robot model because this model performance is very effective and very promising so here we have a transformer model that pre-trained on a big corpus if we want to use this transformer model for our specific task in urdu although it is already pre-trained in urdu we need to fine-tune carefully with imported hyper-parameters so with fine-tuning it's not easy task if we blindly fine-tune our pre-trained model it could even loss the prior knowledge so we have two issues while fine-tuning any transformer model when we fine-tune we have these two issues one is catastrophic forgetting and the other one is overfitting catastrophic forgetting means the models already learn our knowledge by pre-training and if we fine-tune it on another corpus then if we do not deal it properly then it could lose the previous learning so we have to because we unfreeze the all layers of the transformer model and then for learning new knowledge we fine-tune it so it could lose the previous learning the other problem is overfitting that is also related with deep learning domain that choosing the number of epochs for training a model is a combination so if we choose very few epochs it could result in underfitting and if we choose too many epochs it could result in overfitting so we deal with these two problems appropriately the catastrophic forgetting and overfitting these are the this is the list of hyper-parameters which we use for fine-tuning the Udu-Roberta model you can see the list of the parameters and their ranges we applied the grid search methodology to find the optimum values of the parameters so that we can get the best performance from fine-tuning while overfitting problem was dealt we choose the validation because when we split the data we split it into three parts training, validation and test part and the technique mentioned here it's a very common technique and majority of the researcher applied this technique for fine-tuning that the 80% data should be used for the training purposes and 20% use for testing purposes and from the 80% 90% is actually used for training and 10% is used for validation so we use the validation part of the data set to see to analyze the validation loss on the model when we apply the train model on the validation part of the data set we generate the validation loss and we monitor the validation loss and see when model when validation loss decreases and when it is going to increase and continuously increasing so we concluded on this on this matter of overfitting that 5 to 6 epochs are enough for this problem I will describe this matter in the next slides and that how it is possible so fine-tuning process it is common that we need to apply the tokenization then training training any transformer model with a classification layer as far as catastrophic forgetting is concerned I already described that we unfreeze the layers of the transformer model while fine-tuning we have to carefully monitor the learning of the model that it already learns so we finalize the starting learning rate that from which learning rate we should start our model fine-tuning so that it could keep the previous learning so we will be able to learn new knowledge that is related to threatening content and targeted identification so we tried several learning rates those are given here 3 e raised to power 4 and above but while applying the learning rate we conclude we reach on a conclusion that the fine-tuning of the Udu Roberta leads to conversion failure so we obtain the best performance with the 2 e raised to power minus 5 learning rate that is helpful to handle the problem of catastrophic forgetting the next point is which baselines and comparable models we use to compare our proposed methodology so that we can compare fairly that how our proposed model is performing so there was only one study exist in the prior approaches that also it has the same problem of threatening content and its threatening content and targeted identification but with this approach the problem was with their annotated data set their data set was actually not threatening those were basically the the data set was actually offensive data set and they use it for the threatening identification problem so we already described that we designed the new data set annotated it on the two level threatening versus not threatening and targeted identification but for this bench for this previous benchmark or baseline we regenerate their their results on our data set to compare it fairly with our proposed methodology and we also design our new comparable models so that we can we must compare our proposed methodology with enough comparables so latent semantic analysis and a bag of word approaches feature engineering approaches that are considered in the benchmark word engram, corrector engram fast tech embeddings these feature engineering approaches were present and we also utilize these state-of-the-art machine learning models so because these models already demonstrated significant performance in related NLP tasks and these are the performance measures to evaluate the performance of the classifiers coming to the next point okay so here I added the training and validation results obtained by fine-tuning the Urdu Roberta for threatening versus non-threatening tasks you can see the two sequence stands for upright 64 and 128 with three batch sizes 8, 16 and 32 and five epochs results are added here the data related to the validation loss training loss, validation accuracy and their time we can see that the training loss is continuously decreasing it's mean when we applied, when we train the model it continuously train but when we applied the train model on the validation part we can see that in the first three instances sorry for first three epochs the model learn model behave appropriately then after that the validation loss start increasing this is the common common behavior of train model on with all combinations so if we see this this behavior of validation and training loss individually we can see that the behavior of the training loss is continuously decreasing and we can see with different sequence length and different batch sizes with sequence length of 64 and batch size of 8, 16 and 32 and for sequence length of 128, 8, 16 and 32 batch size is continuously decreasing it's mean on every epoch the model is continuously learning but when we when we applied the training train model on the validation part of the dataset we can see that for the first three epochs the model the model behavior is that the model is basically performing very well and then after that the validation loss start increasing up to the five epochs so we reach on the conclusion that five epochs are enough even four epochs are also enough to test this model on the test part of the dataset because after this the validation loss is continuously increasing and there is no there is no because if we try more epochs then our model could overfit maybe it could give better results but it could overfit on the validation part of the dataset and then when we try it on the test part it will perform very badly so we did not apply we only apply the five epochs the reason is I already described with you that is for threatening not threatening task so before coming to this first we trained then validated and then tested this validated fine-tuned to beta model for testing part of the dataset let me describe you the results obtained from the baseline and from the comparable model here you can see that when the five machine learning algorithms we applied linear regression sorry logistic regression as far as SVM, KNIR, SNABLE and NIBE so these five machine learning models were applied for every for each type of features a word unigram byground trigram and their combined effect then corrector unigram byground trigram I just added only those results which are some which crosses some threshold so also we can see the FOSTEX performance bag of word latent semantic analysis here we can see that the logistic regression as compared to the other machine learning models applied machine learning model outperform it gives the best performance so this is one of the findings related to the baseline and comparable model and the other point is as far as threatening versus non-threatening classification is concerned in baseline and comparable model we got the best performance with corrector five ground here we can see the performance because this performance is in accuracy and our data set is balanced data set in addition we also compute other metrics that are perceived in macro F1 so here the comparison of the proposed methodology proposed framework for threatening versus non-threatening and the baseline were compared we can see the performance of the various classification models and in proposed section we can see that we got the best performance with 64 sequence length and with 8 batch size and the performance is 87.5% accuracy and if we consider the micro F1 that is 87.8% and with corrector five ground the performance is 85.83% so the proposed framework that is based on not on handicraft features but basically based on automated feature generation methodology it outperformed the other comparable and even the baseline the next part is the target identification of the of the threatening tweets that which target is being addressed either individual or group so here I added again the training and validation results of fine tuning Roberta model for target identification we can see the performance of the training loss, validation loss, validation accuracy and the number of epochs if we closely analyze the validation loss here for threatening versus non-threatening task the validation loss start increasing from third epochs but here you can see that the validation on validation loss means the when we write the train model on the validation part of the data set the validation loss start increasing from the third epochs up to the five epochs so that is the point related to the overfitting that if we try more epochs if we try 10 epochs then our model will definitely overfit because validation loss is not decreasing then the comparison of the baseline comparable model for target identification task with the proposed methodology here we can see that as compared to the threatening versus non-threatening classification task for target identification the sequence length of 128 with size of 8 outperformed and provided 83.20% at micro F1 and it outperformed Sorry about that we just have a couple of few minutes if you can try to I am just going to point out because it's just three slides okay so by concluding the talk I can say that we designed the significant threatening and target identification framework using the contextual semantic embeddings that are basically designed using the pretent transformer model but with fine tuning to handle the ambiguity and the complexity issues of the Urdu language the proposed framework demonstrated benchmark performance in comparison with the comparable and the baseline on the top of all it is the proposed framework is basically based on the automated feature generation technique known handcrafted feature and the transformer model can capture the actual context of the language being used to threat someone so the finding of this could be helpful for law enforcement organization for identification of this type of unwanted material that is threatening content and target identification in Urdu language while talking about the future prospects according to my point of view I supply the future prospects in three points if we deal the interpretability of the trained models and the tested models for low resource languages we will face the issue for example because each low resource language has a different way of creating context to describe an opinion the next issue that could arise is that the definitions of these different form of hate speech these definitions overlap so we need appropriate categorization of these various type of hate speech for low resource languages because the definitions overlap and then the classification system could not be effective or efficient the third future prospect and the challenge could be that as I described earlier that people usually use multiple script for a single language to describe their opinion so we there must be a problem of code mixing and if we are going to design an efficient code mix contented identification problem that is not an easy task so that is also a challenging task I am not talking about simple code mix contented identification I am talking about the efficient that should be efficient so that's all from my side and any questions from the audience Mohammad thank you very much nice presentation on a very important topic of course for model NLP first I just would like to ask if there is any questions from the audience let me then just start the formulation of your task is classification so you try to cast toxicity hate speech problem as a classification but how about alternative formulations let's say you mentioned that hate speech always has a target how about detecting precisely what is the target and what exactly insults or other aspects particular hate speech attributes we used or how about a generation of hate speech using LLMs and preventing this so could you comment on this alternative let's say directions of work I use the handcrafted features and also the language model too basically the problem is not hate speech it's a type of hate speech that is the threatening content so I applied both both models both type of model handcrafted feature generation techniques yes that's what I mean but the question is not about how do you solve the problem but whether okay we need classifiers for hate speech for toxicity but then maybe these automatic systems machine learning systems should be also acting differently let's say being tagging systems every token of systems so technically not as classifiers but maybe as sequence tagging or rewriting toxic speech into something non-toxic which would be kind of a machine translation or sequence to sequence so what do you think but my topic was just classification I was not considering the sequence to sequence phenomena because that's why I use the annotated data set so that the classifier have opportunity to learn the exact context of the language and that is the specifically language bound classifier that is for Urdu Arabic style that could not be we could not say that this type of particular classifier could be applied as it is for other low resource languages for other low resource languages we have to consider the language context what are the opportunities what are the what are the preprocessing and what are the other things are available we have to consider them and it's a monolingual approach it's not a multilingual although I have designed a multilingual approach but as far as low resource languages usually monolingual approaches are designed so it is just specifically related to the specific language and specific style so okay yes yes so I'm sorry to interrupt cut this short but we are really out of time because of some shift so yeah I suggest you just to show your contacts and for those who are interested can contact you on this work and ask questions directly okay thank you very much let's thank Mohammed for his presentation and proceed to for the talk chaired by Andrei Kruz okay thank you so much and then thank you for the organizing committee and Dr. Dimitri who provided me this opportunity okay so this is our fully remote session and it's also streamed on YouTube so it's not limited to only those present in Zoom but yeah so the first talk is about automatic aspect extraction from scientific texts by Iliana Bruges and Tatiana Batura and as far as I understand Anna will be presenting is that true can you unmute yourself yes can you hear me thanks yes yes so the floor is yours can you try to share your screen so hello everyone my name is Anna and I'm going to present our paper which is called automatic aspect extraction from scientific texts as the number of published search papers increases there is a growing need in tools that automatically extract information from them for example we might need to extract such information as the task of the research the author's contribution the use methods and the conclusion of the study we suggest calling these main points of paper its aspects however even though Russian is among the languages most commonly used in science there is a sparse amount of aspect extraction tools for Russian and most of them focus on certain domains such as medicine or commuter science to address this in our research we aim to create a cross-domain data set of Russian language scientific texts and to propose a tool for automatic aspect extraction from Russian scientific texts of any domain let's start with the data it contains 200 abstracts two papers of scientific domains namely psychology physics, medicine, mathematics computer science, linguistics journalism, pedagogy law and history in this text we identified four types of aspects task, contribution method and conclusion here is an example of an annotated text as you can see aspects do not cover the whole text and aspects can be nested for example in this case task and method are nested inside the contribution aspect overall we identified 836 aspects almost half of them being the contribution aspect this might be due to the fact that abstracts are written to give an idea of the author's contribution however in some domains the conclusion aspect prevails for example medicine with papers describing results of clinical studies or history with papers describing results of archaeological expeditions as for the task aspect we discovered that in some domains especially in humanities we are not talking about the tasks themselves but rather about some problematic issues or research objects so it was decided to attribute them to the task aspect as well and as for the method aspect it is most usually mentioned in papers on natural and exact sciences an average length of an aspect is 12 tokens but it mostly depends on the aspect type so task and method are rather short and they expressed in short terms or phrases whereas conclusion and contribution are rather long and they are expressed in full sentences or clauses so let's move on to the algorithm for this task we fine-tuned a BERT model on a multi-class, multi-label token classification task so for each token we select up to two most probable aspects the probabilities of which are higher than the threshold and if none of them are higher than the threshold it means that the aspect is not assigned to any the token is not assigned to any aspect at all after that the neighboring tokens assigned to one aspect are united into spans and to the spans we apply some restricts to enhance the aspect boundary detection these restricts usually remove unnecessary words or add missing ones to the extracted aspects finally aspects expressed in nominal phrases are put into the nominative case so here is what we get as results on the left for example of automatic aspect extraction and you can compare it to the manual annotation on the right in this task in this case the model performed quite well but not perfectly for example the extracted conclusion is rather incomplete but it still expresses the main point to find the best model we conducted a number of experiments which included using different models and freezing weights and putting some layers on top but the best results were shown by multilingual birds fine-tuned to our data with just a linear layer for classification the fact that a multilingual model outperformed monolingual specialized models is quite surprising but to find its solutions some new experiments are needed and we plan to conduct them in future by now these are the best metrics for the best model and the best extracted aspect is contribution as it is the most frequent aspect in the dataset and the worst extracted aspect is task which might be too due to its native apart from metrics for individual tokens we used exact match ratio which is lower than the other metrics so we still have some problems with aspect boundary detection finally we conducted cross-domain experiments to see how our model performs on unseen domains for each experiment we used 10 domains to train the model and one to test it and the obtained results proved that our model was able to generalize to new domains so as a result in our study we created a cross-domain dataset of Russian language scientific text with manual aspect annotation and proposed a tool for automatic aspects extraction from Russian scientific texts of any domain the code and the dataset are available by this link and the result thank you for your attention yes thanks a lot so since I hear some uploads from Yerevan I'm not sure maybe there are some questions from the audience on site before we get to questions from the audience online so at least I do have a question you can proceed okay right so again thanks for the talk my question is mostly related to the dataset that you created especially these aspects that you choose to use so for example it's not like I'm looking at the dataset right now in your GitHub repository and it's not quite clear what's the difference between contributions and conclusions because it's obvious that sometimes well not sometimes but often the conclusion of the paper contains the contributions right and I'm looking at some examples where I myself would not be entirely sure whether this is a contribution or a conclusion so how did you like choose between these two labels when annotating I mean we mostly identified as contributions something that authors have done and when they write that we have proposed something, research something or something like that and we identified conclusion when they write about the exact conclusion that was reached during their research so like one of the examples from the linguistics subset so the sentence this is labeled as contribution and then comes the sentence labeled as conclusion and the sentences are so like the authors have shown something and this is labeled as conclusion and then another sort of description of what the authors have done is labeled as contribution time for the conclusion aspect we also used some words, markers like and so on and we mostly identified as conclusion the clause which follows the main sentence which is so I mean in this case it is more obvious that this is a conclusion and when we are talking about something like the first sentence you mentioned so how I see it in this sentence they write that they propose to consider this problem in this way so yeah and you report the average numerator agreement in the paper but to maybe remember what was the agreement for these two aspects I mean contribution and conclusion we did not measure agreement for pairs of aspects it was just measured for the whole dataset but I guess you have the numbers for every specific aspect because you report the average numerator agreement over all four aspects so if you average it then it means that you have four estimations I mean for values well yeah I guess I haven't but I just I don't think that I paid attention to the intermediate results it was just accounted it was just averaged and I think that maybe I should have paid attention because I think there might be some interesting discoveries about which pairs of aspects are most usually confused okay I see thanks yeah I believe the dataset that you released definitely is going to be very useful okay anyway any questions from the last chat yeah I guess you can just come to the microphone and start speaking because I don't see you anyway well I see something well if you come to the microphone it will be better I guess no you can proceed there is no question from the audience for this one so okay then let's thank the speakers again thank you thanks a lot so yeah the next talk is supposed to be Promptuning for Targeted Sentiment Analysis in Russian by Yuliana Salomatin and Natalia Lukashevich okay I'm going to present our paper which is called Promptuning for Targeted Sentiment Analysis in Russian and first of all we need to define what Targeted Sentiment Analysis is and how it is different from general sentiment analysis in fact it is often important to take into account the relationships between the participants of the situation for example X offended Y and so on and this is what Targeted Sentiment Analysis is so we have a target and we have some attitude that is being expressed towards it and there are very few studies on this topic using Russian language material and it is important to mention that Targeted Sentiment Analysis is particularly relevant for the news discourse and news texts are more difficult to analyze in terms of sentiment as compared to with reviews for example not because of this target topic but also because of the predominance of neutral polarities since journalists always try to be as neutral as possible and that's why some sentiments are expressed implicitly which means there are no expressive sentiment words but just some underlying meanings some facts that can be defined as sentiments and so earlier this year the competition Roussant NE was organized and the task was to deal with the Targeted Sentiment Analysis on Russian data and the current study suggests prompt-based learning for this task and for the Blackbone model we used Rourbet large model and the experiments were based on the question answering approach which means that the task was formulated as a question in a natural language and then it was fed into the model and the expected output of the class label positive negative or neutral and it is important to mention that what worked best is fine-tuning and prompt-tuning both and I'm going to briefly overview the methods that we implemented during our experiments and first of all we are all very familiar with the fine-tuning approach so a prompting approach that was recently suggested the idea behind that is that when we fine-tune the model for the downstream task we can formulate a prompt in such a manner that the downstream task will be very similar to the pre-training task and this can boost the model's performance and another problem with this kind of tuning is that it is hard to choose the prompt manually because we can change one token in this prompt and this can significantly affect the result and that's why we can tune the prompt just like we tune the whole model or if we deal with a very large model like GPT-3 we can tune the prompt instead of fine-tuning the model and after that many different modifications of this approach were suggested for example prompt-tuning with rules it means that we can mask not only the class label but also some other tokens that help explain the task to the model better and then we can aggregate the predictions of our conductive formal form and get the final class label another approach is knowledgeable prompt-tuning it deals with the verbalizer verbalizer is basically a list of class names which are then mapped with the labels of the class and these names are predicted during the language modeling block and knowledgeable prompt-tuning means that we can predict not only the labels of the class but also some words related to them which are extracted from some external linguistic research so in this study we implemented both manual prompts, prompting approach and prompt-tuning approaches that I just described and also we implemented the approach of mixed templates which means that some tokens in the prompt can be fixed and others can be tuned and the data is which it was pre-labeled with named entities and then it was labeled with sentiments and with relationships between their named entities and for the competition it was preprocessed for example the only used sentences with non-contradictionary cases it means that X is left by Y but hated by Z it is contradictory case which in these cases were excluded and also we only dealt with sentence level and not with document level and for evaluation in this research we just conducted three-fold cross-validation and then we tested our model on the root-send-need split which was provided by the organizers of the competition and I would like to say that the baseline model for the competition was without any modifications and the results are presented on the slide which was used apart from just the F score F1 score also the F1 score that was averaged across only the positive and negative class since these two classes are particularly interesting for this task and also I would like to acknowledge the authors of this paper about Open Prompt since they released a very helpful tool for implementing different prompt-tuning strategies out of the box which really helps using them for using any prompt-tuning approach for the downstream task just by loading their pretrained model from Hugginface and formulating the template of the prompt and the first series of experiments is a new prompt-based experiment and here we tested two variables the prompt type and the way we handle class imbalance since, as I already mentioned news texts contain many neutral polarities and not many positive or negative ones and we used a prompt as just a target word about how do they feel about X and for handling class imbalance we first of all try to not use any other anything for this then we try to calculate class weights in the during the in the loss function and also to augment data via back translation and replacing some tokens with contextually close other tokens then the mixed template is presented on the slide here the soft are the tunable tokens and text are the fixed tokens which were just the same during the whole training stage and the verbalizer class names then we implemented prompt training with rules and here we also tested how the initialization of the prompt can affect the result and in the first template we try to focus on the fact that there is some participant of the situation mentioned in the content from which the model can derive the attitude and in the second plate we emphasize the fact that the sentiment can be expressed both implicitly and explicitly and the last approach is notable prompt tuning and for this we utilized root center legs and in the first strategy we try to collect words for the verbalizes in such a way that for both negative and positive sentiments their classes could overlap since there are some words that can have different meanings in different contexts and can have different sentiments in different contexts and strategies we made sure that there are no overlapping and the positive class is always prioritized since in the previous experiments we saw that the results for the positive class are always worse than for the negative one and so it means that for example some words like shame murder negative connotation that's why it is negative class and so on and the results are presented here and we can see first of all that no no method to handle class imbalance showed good result and some of them like augmentation are very time consuming and they do not give some performance boost so they are not really suitable for the task and prompting approach works much better and the best model that showed the best result during our experiments is knowledgeable prompt tuning with the other first strategy and so we selected this best model and then evaluated it on the data split from the competition and the results can be compared to the third place however the models from the top of the leader broad leveraged its assembly methods and these methods are extremely computationally intensive because they are ensembles of transformers and so the prompting and prompt tuning approach works really good not only in terms of the quality but also in terms of the computationally intensively and so in the current study we researched the task of targeted sentiment analysis in Russia and we tested different strategies for both hard prompts and soft prompts and we also saw that prompt tuning along with fine tuning surpasses vanilla fine tuning in manual prompting and the best model that showed really good results was the model based on knowledgeable prompt tuning and that's it thank you for your attention if you have any questions please feel free to ask them thanks a lot Filiana so are there any questions in Zoom let's for a change start with the online audience you can raise your hand or just unmute your microphone and as I said if there are any questions in the on-site from the on-site audience come up to the mic meanwhile maybe a very silly question for me since you use prompting anyway so why have you decided to use birdlike encoder models not generative decoder or encoder decoder models and don't you think that using generative models would improve the performance of your approach thank you for your question yeah I was considering that but first I started with the rule bird model because this was before the whole thing with chat dpt and so on and bird models were more suitable for classification tasks and also for the purposes of computational possibilities and then the experiments showed that Rue Roberta was given even better results and then the chat dpt appeared and I also had some experiments with chat dpt which were not very successful and this is just a topic for another conversation why they were not that successful but yeah I think that it is possible that decoder only models when promptuned can be very suitable for this task but I also wonder as far as I know the participants of the competition no one used decoder models maybe they also conducted some experiments but yeah thank you I think that there is room for improvement for other experiments on that yeah thanks and what about decoder models like T5 etc you didn't try them either no I didn't try them also okay thanks okay thank you I guess we are way out of time so we probably should move on with the next talk but yeah thanks again Edana that was very interesting and the next talk is supposed to be the battle information presentations comparing sentiment and semantic features for forecasting market trends yeah and I guess Andrei Zaichenka is here in zoom so again greetings everyone I would introduce to you our paper comparing sentiment and semantic features for forecasting market trends in recent years several studies have applied deep learning and OP techniques to financial data including news articles, social media posts and quarterly financial reports in order to predict price movements despite its significance most researchers rely on sentiment analysis as the primary additional feature living little exploration of the potential of semantics and context hidden in a text in this paper we aim to fill this gap in research by testing the hypothesis that semantic features are important for stock price prediction our approach uses sentence embeddings extracted from Twitter data to capture extra information and contextual relationships with financial market trends and then we compare our approach to traditional sentiment based solutions to evaluate its performance In the introduction to the paper we made a cla