PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation

• URL: http://arxiv.org/abs/2303.01795v1
• Date: Fri, 3 Mar 2023 09:13:23 GMT
• Title: PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
• Authors: Xiaojie Gu, Renze Lou, Lin Sun, Shangxin Li
• Abstract summary: Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation. We devise a novel position-aware graph to encode the entire conversation, fully modeling causal relations among utterances. Our method consistently achieves state-of-the-art performance on two challenging test sets.
• Score: 3.8754794750431447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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