PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
Xiaojie Gu, Renze Lou, Lin Sun, Shangxin Li
Abstract
Conversational Causal Emotion Entailment (C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>E<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) 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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
BibTeX
@inproceedings{icassp2023_pageapositionawa,
title = {PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation},
author = {Xiaojie Gu and Renze Lou and Lin Sun and Shangxin Li},
booktitle = {ICASSP 2023},
year = {2023}
}