Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
This work addresses the problem of understanding emotions and their causes in conversations for human-computer interaction agents, but it is incremental as it builds on existing methods for a specific competition task.
The authors tackled the task of Multimodal Emotion-Cause Pair Extraction in Conversations by proposing a multi-stage framework that uses Llama-2-based InstructERC for emotion recognition and a two-stream attention model for extracting emotion-cause pairs, achieving first place in both subtasks of the SemEval-2024 competition.
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.