Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 
 # Emotion-Cause-in-Friends (ECF)
 
-For the task named Multimodal Emotion-Cause Pair Extraction in Conversation, we accordingly construct a multimodal conversational emotion cause dataset ECF, which contains 9,794 multimodal emotion-cause pairs among 13,619 utterances in the *Friends* sitcom.
+For the task named Multimodal Emotion-Cause Pair Extraction in Conversation, we accordingly construct a multimodal conversational emotion cause dataset ECF (1.0). For SemEval 2024 task 3, we have furthermore annotated an extended test set as the evaluation data. *Note*: ECF (1.0) and the extended test set for SemEval evaluation constitute ECF 2.0.
 
 For more details, please refer to our GitHub: 
 
@@ -17,16 +17,24 @@ For more details, please refer to our GitHub:
 
 ## Dataset Statistics
 
-| Item                            | Train | Dev   | Test  | Total  |
-| ------------------------------- | ----- | ----- | ----- | ------ |
-| Conversations                   | 1001  | 112   | 261   | 1,374  |
-| Utterances                      | 9,966 | 1,087 | 2,566 | 13,619 |
-| Emotion (utterances)            | 5,577 | 668   | 1,445 | 7,690  |
-| Emotion-cause (utterance) pairs | 7,055 | 866   | 1,873 | 9,794  |
+| Item                            | Train | Dev   | Test  | Total  |  Evaluation Data for SemEval-2024 Task 3  |
+| ------------------------------- | ----- | ----- | ----- | ------ | ------ |
+| Conversations                   | 1001  | 112   | 261   | 1,374  | 341  |
+| Utterances                      | 9,966 | 1,087 | 2,566 | 13,619 | 3,101 |
+| Emotion (utterances)            | 5,577 | 668   | 1,445 | 7,690  | 1,821  |
+| Emotion-cause (utterance) pairs | 7,055 | 866   | 1,873 | 9,794  | 2,462  |
+
+## Supported Tasks
+
+- Multimodal Emotion Recognition in Conversation (ERC)
+- Causal/Cause Span Extraction (CSE)
+- Emotion Cause Extraction (ECE) / Causal Emotion Entailment (CEE)
+- Multimodal Emotion-Cause Pair Extraction in Conversation (MECPE)
+- ...
 
 ## About Multimodal Data   
 
-⚠️ Due to potential copyright issues with the TV show "Friends", we do not provide pre-segmented video clips. 
+⚠️ Due to potential copyright issues with the TV show "Friends", we cannot provide pre-segmented video clips. 
 
 If you need to utilize multimodal data, you may consider the following options:
 
@@ -35,8 +43,7 @@ If you need to utilize multimodal data, you may consider the following options:
     - [`video_embedding_4096.npy`](https://drive.google.com/file/d/1NGSsiQYDTqgen_g9qndSuha29JA60x14/view?usp=share_link): the embedding table composed of the 4096-dimensional visual features of each utterances extracted with 3D-CNN
     - Please note that the above features only include the original ECF (1.0) dataset; the SemEval evaluation data is not included. If needed, you can contact us, and we will do our best to release new features.
   
-2. Since ECF is constructed based on the MELD dataset, you can download the raw video clips from [MELD](https://github.com/declare-lab/MELD). 
-Most utterances in ECF align with MELD. However, **we have made certain modifications to MELD's raw data while constructing ECF, including but not limited to editing utterance text, adjusting timestamps, and adding or removing utterances**. Therefore, some timestamps provided in ECF have been corrected, and there are also new utterances that cannot be found in MELD. Given this, we recommend option (3) if feasible.
+2. You can download the raw video clips from [MELD](https://github.com/declare-lab/MELD). Since ECF (1.0) is constructed based on the MELD dataset, most utterances in ECF (1.0) correspond to those in MELD. The correspondence can be found in the file [all_data_pair_ECFvsMELD.txt](https://github.com/NUSTM/MECPE/blob/main/data/all_data_pair_ECFvsMELD.txt). However, **we have made certain modifications to MELD's raw data while constructing ECF, including but not limited to editing utterance text, adjusting timestamps, and adding or removing utterances**. Therefore, some timestamps provided in ECF (1.0) have been corrected and may differ from those in MELD. There are also new utterances that cannot be found in MELD. Given this, we recommend option (3) if feasible.
 
 3. Download the raw videos of _Friends_ from the website, and use the FFmpeg toolkit to extract audio-visual clips of each utterance based on the timestamps we provide.