mlsanityData to 7 Oct 2026

SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations

arXiv 2405.13049 ↗First posted 19 May 2024cs.CL cs.AI cs.MMcited by 11

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Abstract (arXiv)

The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great importance in many application scenarios. We organize SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, which aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE). The shared task has attracted 143 registrations and 216 successful submissions. In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 37 of its 73 references that are in the map. First posted on arXiv 19 May 2024.

Builds on

37 of 73 references.

References · 37

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25 more
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  10. 2021EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTacited by 59
  11. 2019M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cuescited by 56
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  14. 2021Multimodal Emotion-Cause Pair Extraction in Conversationscited by 30
  15. 2019RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extractioncited by 24
  16. 2019From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identificationcited by 17
  17. 2024MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Modelscited by 9
  18. 2022TSAM: A Two-Stream Attention Model for Causal Emotion Entailmentcited by 6
  19. 2022Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversationscited by 3
  20. 2024JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama modelscited by 2
  21. 2024nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion Statescited by 2
  22. 2024PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversationscited by 2
  23. 2024Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversationscited by 2
  24. 2024LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Taskcited by 1
  25. 2024LyS at SemEval-2024 Task 3: An Early Prototype for End-to-End Multimodal Emotion Linking as Graph-Based Parsingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 5.

11 papers.

  1. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 24
  2. 2024Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understandingcited by 17
  3. 2024NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversationscited by 5
  4. 2025Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Modelscited by 4
  5. 2024EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understandingcited by 3
  6. 2025AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysiscited by 0
  7. 2026A Baseline Multimodal Approach to Emotion Recognition in Conversationscited by 0
  8. 2026NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Surveycited by 0
  9. 2026They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanationscited by 0
  10. 2026Learning Robust Pair Confidence for Multimodal Emotion-Cause Pair Extractioncited by 0
  11. 2026Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theorycited by 0