PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
This addresses conversational emotion analysis for applications like mental health monitoring, though it appears incremental as it builds on existing graph-based methods with position encoding improvements.
The paper tackles the problem of identifying emotion causes in conversations by proposing a position-aware graph model that accounts for utterance order and speaker relations, achieving state-of-the-art performance on two challenging test sets.
Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation. The order of utterances in the conversation affects the causal inference. However, most current position encoding strategies ignore the order relation among utterances and speakers. To address the issue, we devise a novel position-aware graph to encode the entire conversation, fully modeling causal relations among utterances. The comprehensive experiments show that our method consistently achieves state-of-the-art performance on two challenging test sets, proving the effectiveness of our model. Our source code is available on Github: https://github.com/XiaojieGu/PAGE.