基于多模态分析，精准提取对话中情绪与原因的对应关系。
AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
- 分三阶段建模：嵌入提取、成对抽取与问答式定位。
- 在23支队伍中位列第10（文本）和第6（多模态）。
- 适合关注情感分析与对话理解的研究者。
SemEval-2024 Task 3 包含两个子任务，聚焦对话情境中的情绪-原因对抽取。子任务1针对文本情绪-原因对的抽取，其中原因以对话中的文本片段形式标注；子任务2则扩展至包含语言、音频和视觉的多模态线索，考虑原因可能不完全体现在文本中。我们提出的模型分为三个核心部分：(i) 嵌入提取，(ii) 情绪-原因对抽取与情绪分类，(iii) 找到成对后通过问答方式精确定位原因。通过采用前沿技术并在任务特定数据集上微调，模型有效解析了对话中的复杂动态，捕捉情绪表达中的因果细微信号。我们的团队 AIMA 在该竞赛中表现优异，在23支参赛队伍中，子任务1排名第十，子任务2排名第六。
原文摘要 · Abstract (English)
The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction & emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。