Summary of Semeval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations, by Fanfan Wang et al.
SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
by Fanfan Wang, Heqing Ma, Jianfei Yu, Rui Xia, Erik Cambria
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The ability to understand emotions is crucial for artificial intelligence to be human-like, as emotions significantly impact cognition, decision-making, and social interactions. SemEval-2024 Task 3, Multimodal Emotion Cause Analysis in Conversations, aims to extract emotion-cause pairs from conversations under different modalities (textual and multimodal). The task consists of two subtasks: TECPE and MECPE. A total of 143 teams registered for the shared task, submitting 216 systems. This paper introduces the task, dataset, evaluation settings, summarizes top team systems, and discusses participant findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Emotions are important in artificial intelligence because they influence how we think and make decisions. A new challenge called Multimodal Emotion Cause Analysis in Conversations helps computers understand emotions better. The challenge involves identifying emotions and the reasons behind them in conversations, which is useful for many applications. Many teams took part in this challenge, and their systems were tested to see how well they worked. |
