用指令微调大模型提升对话情绪原因配对识别能力
NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations
- 针对情绪与原因配对任务，设计双模块系统分离识别与抽取
- 通过情绪-原因感知的指令微调，实现34.71%加权F1得分
- 适合关注多模态情感分析与大模型微调的研究者
本文介绍为SemEval-2024任务3：对话中的多模态情绪-原因分析所构建的系统架构。项目聚焦于子任务2——带情绪类别的多模态情绪-原因配对抽取（MECPE-Cat），提出一种双组件系统以应对该任务的独特挑战。将任务拆分为对话情绪识别（ERC）和情绪-原因配对抽取（ECPE）两个子任务。利用大语言模型（LLM）在多种自然语言处理任务中表现出的先进性能，设计了一种情绪-原因感知的指令微调方法，增强模型对情绪及其对应因果推理的理解。该方法有效应对了MECPE-Cat的复杂性，在基准测试中取得34.71%的加权平均F1分数，排名第二。代码与实验数据已公开可复现。
原文摘要 · Abstract (English)
This paper describes the architecture of our system developed for Task 3 of SemEval-2024: Multimodal Emotion-Cause Analysis in Conversations. Our project targets the challenges of subtask 2, dedicated to Multimodal Emotion-Cause Pair Extraction with Emotion Category (MECPE-Cat), and constructs a dual-component system tailored to the unique challenges of this task. We divide the task into two subtasks: emotion recognition in conversation (ERC) and emotion-cause pair extraction (ECPE). To address these subtasks, we capitalize on the abilities of Large Language Models (LLMs), which have consistently demonstrated state-of-the-art performance across various natural language processing tasks and domains. Most importantly, we design an approach of emotion-cause-aware instruction-tuning for LLMs, to enhance the perception of the emotions with their corresponding causal rationales. Our method enables us to adeptly navigate the complexities of MECPE-Cat, achieving a weighted average 34.71% F1 score of the task, and securing the 2nd rank on the leaderboard. The code and metadata to reproduce our experiments are all made publicly available.
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