MIPS在SemEval-2024任务3中的研究：基于多模态语言模型的对话多模态情绪-原因对抽取
MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models
AI总结：
本研究针对SemEval 2024任务3的对话多模态情绪原因分析子任务，提出整合多模态特征的MER-MCE框架，取得0.3435的加权F1分，位列第三名。
摘要：
本文介绍了我们在SemEval 2024任务3对话多模态情绪原因分析子任务2中的获奖参赛方案。我们提出了一种新颖的多模态情绪识别与多模态情绪原因抽取（MER-MCE）框架，该框架使用专用情绪编码器整合文本、音频和视觉模态。我们的方法与顶尖参赛团队的区别在于，利用模态特定特征来增强情绪理解和因果推理能力。实验评估证明了我们多模态方法的优势，我们的参赛方案取得了0.3435的加权F1分数，排名第三，仅落后第一名团队0.0339，落后第二名团队0.0025。项目地址：https://github.com/MIPS-COLT/MER-MCE.git
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
