This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations. We propose a novel Multimodal Emotion Recognition and Multimodal Emotion Cause Extraction (MER-MCE) framework that integrates text, audio, and visual modalities using specialized emotion encoders. Our approach sets itself apart from top-performing teams by leveraging modality-specific features for enhanced emotion understanding and causality inference. Experimental evaluation demonstrates the advantages of our multimodal approach, with our submission achieving a competitive weighted F1 score of 0.3435, ranking third with a margin of only 0.0339 behind the 1st team and 0.0025 behind the 2nd team. Project: https://github.com/MIPS-COLT/MER-MCE.git
@article{arxiv.2404.00511,
title = {MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models},
author = {Zebang Cheng and Fuqiang Niu and Yuxiang Lin and Zhi-Qi Cheng and Bowen Zhang and Xiaojiang Peng},
journal= {arXiv preprint arXiv:2404.00511},
year = {2024}
}
Comments
Ranked 3rd in SemEval '24 Task 3 with F1 of 0.3435, close to 1st & 2nd by 0.0339 & 0.0025