A review of evaluation techniques for social dialogue systems
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paper_title: Spoken Dialogue Technology: Toward the Conversational User Interface

paper_content:

What is a Spoken Dialogue System?-. The Components of a Spoken Dialogue System-. Describing Dialogue-. Developing a Spoken Dialogue System: The Dialogue Engineering Lifecycle-. Directed Dialogue Systems-. Developing Directed Dialogue Systems using the CSLU Toolkit-. Developing Directed Dialogue Systems using VoiceXML-. Mixed-Initiative Dialogue Systems-. Developing Mixed - Initiative Dialogue Systems using VoiceXML-. Developing Mixed - Initiative Dialogue Systems using the CU Communicator System-. Conversational Agents-. Future Developments
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paper_title: Metrics and Evaluation of Spoken Dialogue Systems

paper_content:

The ultimate goal of an evaluation framework is to determine a dialogue system’s performance, which can be defined as “the ability of a system to provide the function it has been designed for” [32]. Also important, particularly for industrial systems, is dialogue quality or usability. To measure usability, one can use subjective measures such as User Satisfaction or likelihood of future use. These subjective metrics are difficult to measure and are dependent on the context and the individual user, whose goal and values may differ from other users. This chapter will survey evaluation frameworks and discuss their advantages and disadvantages. We will examine metrics for evaluating system performance and dialogue quality. We will also discuss evaluation techniques that can be used to automatically detect problems in the dialogue, thus filtering out good dialogues and leaving poor dialogues for further evaluation and investigation [62].
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paper_title: A Neural Conversational Model

paper_content:

Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.
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paper_title: Deep Reinforcement Learning for Dialogue Generation

paper_content:

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.
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paper_title: ROUGE: A Package For Automatic Evaluation Of Summaries

paper_content:

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST.
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paper_title: Bleu: A Method For Automatic Evaluation Of Machine Translation

paper_content:

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
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paper_title: A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

paper_content:

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.
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paper_title: How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

paper_content:

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.
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paper_title: Adversarial Evaluation of Dialogue Models

paper_content:

The recent application of RNN encoder-decoder models has resulted in substantial progress in fully data-driven dialogue systems, but evaluation remains a challenge. An adversarial loss could be a way to directly evaluate the extent to which generated dialogue responses sound like they came from a human. This could reduce the need for human evaluation, while more directly evaluating on a generative task. In this work, we investigate this idea by training an RNN to discriminate a dialogue model's samples from human-generated samples. Although we find some evidence this setup could be viable, we also note that many issues remain in its practical application. We discuss both aspects and conclude that future work is warranted.
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paper_title: Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

paper_content:

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.
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paper_title: Deep Reinforcement Learning for Dialogue Generation

paper_content:

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.
---
paper_title: Deep Reinforcement Learning for Dialogue Generation

paper_content:

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.
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Title: A Review of Evaluation Techniques for Social Dialogue Systems

Section 1: INTRODUCTION
Description 1: Introduce the increasing interest in non-task-oriented social dialogue systems, the challenges in their evaluation, and the common methods used.

Section 2: AUTOMATIC METRICS
Description 2: Discuss the popularity, advantages, and various types of automatic evaluation methods for social dialogue systems.

Section 3: Word-Overlap Metrics
Description 3: Explain the use of word-overlap metrics such as BLEU and ROUGE, their limitations, and the need for alternative evaluation methods.

Section 4: Machine Learning Methods for Dialogue Evaluation
Description 4: Describe discriminative models and classification models used for dialogue evaluation, along with their respective advantages and challenges.

Section 5: Reward-based Metrics
Description 5: Explore reinforcement learning-based models, and detail the evaluation functions and metrics at both turn-level and system-level.

Section 6: CONCLUSION AND DISCUSSION
Description 6: Summarize the current state of evaluation methods, highlight their limitations, and suggest directions for future work based on recent research data.