Directed Acyclic Graph Network for Conversational Emotion Recognition
Weizhou Shen, Siyue Wu, Yunyi Yang, Xiaojun Quan
Abstract
The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.
BibTeX
@inproceedings{shen-etal-2021-directed,
title = "Directed Acyclic Graph Network for Conversational Emotion Recognition",
author = "Shen, Weizhou and
Wu, Siyue and
Yang, Yunyi and
Quan, Xiaojun",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.123/",
doi = "10.18653/v1/2021.acl-long.123",
pages = "1551--1560"
}