In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. Unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, Llama-2-based InstructERC is utilized to extract the emotion category of each utterance in a conversation. After emotion recognition, a two-stream attention model is employed to extract the emotion causal pairs given the target emotion for subtask 2 while MuTEC is employed to extract causal span for subtask 1. Our approach achieved first place for both of the two subtasks in the competition.

翻译：在人机交互中，智能体通过理解人类情感来做出响应至关重要，而揭示情感的成因则更具挑战性。一项名为"对话多模态情感-原因对抽取"的新任务，旨在识别情感并定位因果表达。本研究提出了一种多阶段框架，用于生成情感并在给定目标情感条件下抽取情感-原因对。第一阶段采用基于Llama-2的InstructERC模型提取对话中每个语句的情感类别。完成情感识别后，针对子任务2，使用双流注意力模型在给定目标情感条件下抽取情感-原因对；针对子任务1，则采用MuTEC模型提取因果片段。我们的方法在竞赛中分别获得了两个子任务的第一名。