MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (P19-1)
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| Challenge: | Emotion recognition in conversations has gained popularity due to its potential applications. Until now, a large multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. |
| Approach: | They propose to extend and enhance EmotionLines by combining 13,000 utterances from Friends dialogues with emotion and sentiment labels. |
| Outcome: | The proposed dataset contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. |
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| Challenge: | Emotion is a critical characteristic to distinguish people from machines. |
| Approach: | They propose a dataset with emotions labeling on all utterances in each dialogue . they use Friends TV scripts and Facebook messenger dialogues to collect the data . |
| Outcome: | The proposed dataset is the first with emotions labeling on all utterances in each dialogue based on their textual content. |
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)
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| Challenge: | Emotion and sentiment classification in dialogues has gained popularity in recent times . a number of datasets are imbalanced in representing different emotions and consist of an only single emotion. |
| Approach: | They propose to use a dataset to analyze emotions and sentiments in dialogues . they use text, audio and video to identify the correct emotions with the appropriate intensity and sentiment in an utterance of a dialogue . |
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Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
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MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)
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Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
| Challenge: | Emotion plays a crucial role in human conversation. |
| Approach: | They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance . |
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EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations (2026.findings-acl)
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Haoqin Sun, Jinghua Zhao, Xuechen Wang, Shiwan Zhao, Jiaming Zhou, Hui Wang, Xi Yang, Yequan Wang, Yonghua Lin
| Challenge: | Existing datasets face issues such as low quality, limited scale, and incomplete modalities, hindering model performance. |
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| Outcome: | The EmotionTalk dataset spans 23.6 hours of dyadic conversations across diverse scenarios. |
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)
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| Challenge: | Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity. |
| Approach: | They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series. |
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A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)
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| Challenge: | Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion. |
| Approach: | They propose a Dual Contrastive Learning Framework that enhances existing MERC models without additional data. |
| Outcome: | The proposed framework outperforms existing models on two MERC benchmark datasets and shows that it reduces label dependence and enhances emotion-sensitive independent modality features. |
MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships (2020.lrec-1)
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| Challenge: | Existing datasets with emotion and relation labels for dialogues are limited. |
| Approach: | They use a Chinese dialogue dataset to annotate emotions and interpersonal relationships on each utterance. |
| Outcome: | The proposed dataset contains 25,548 utterances from 4,142 dialogues. |
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)
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| Challenge: | Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. |
| Approach: | They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models. |
| Outcome: | The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP. |
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)
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| Challenge: | Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes. |
| Approach: | They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence. |
| Outcome: | The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset. |