S amsung Research C hina- B eijing at S em E val-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations (2024)

Abstract: 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, LLaMA2-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.

## introduction comprehending emotions plays a vital role in developing artificial intelligence with human-like capabilities, as emotions are inherent to humans and exert a substantial impact on our thinking, choices, and social engagements @xcite . dialogues, being a fundamental mode of human communication, abound with a variety of emotions @xcite @xcite @xcite @xcite . going beyond simple emotion identification, unraveling the underlying catalysts of these emotions within conversations represents a more complex and less-explored challenge @xcite . hence, @xcite introduces a ⋆: equal contributions. : corresponding author. shen zhang is in charge of the basic subtask-emotion recognition in conversation (erc) and haojie zhang is responsible for the pipeline framework and causal pair extraction and causal span extraction subtasks. to address the above problem, wang et al. (2023a) proposed a two-step approach. first, they extract the emotional utterances and causal utterances by a multi-task learning framework and then pair and filter them. @xcite proposes an end-to-end method by leveraging multi-task learning in a pipeline manner. however, these methods still suffer from low evaluation performances. motivated by the phenomenon that the performance of the emotion recognition of utterances in a conversation harnessed by the traditional manner is generally poor, we design a new pipeline framework. firstly we utilize the llama-2-based instruc-terc @xcite to extract the emotion category of each utterance in a conversation. then we consider the emotion causal pair extraction as the causal emotion entailment subtask and employ a two-stream attention model to extract the emotion causal pairs given the target emotion. for the causal span extraction, we employ mutec @xcite which is an end-to-end multi-task learning framework. ## conclusion in this paper, we propose a joint pipeline framework for subtask1 and subtask2. firstly, we utilize the llama-2-based instruct erc model to extract the emotional content of utterances in a conversation. next, we employ a two-stream attention model to identify causal pairs based on the predicted emotional states of the utterances. lastly, we adopt an end-to-end framework using a multi-task learning approach to extract causal spans within a conversation. our approach achieved first place in the competition, and the effectiveness of our approach is further confirmed by the ablation study. in future work, we plan to explore the integration of audio and visual modalities to enhance the performance of the task.