  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.
