@inproceedings{liang-etal-2022-page,
title = "{S}+{PAGE}: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation",
author = "Liang, Chen and
Xu, Jing and
Lin, Yangkun and
Yang, Chong and
Wang, Yongliang",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.12",
pages = "148--157",
abstract = "Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. With the development of graph neural network (GNN), recent state-of-the-art ERC models mostly use GNN to embed the intrinsic structure information of a conversation into the utterance features. In this paper, we propose a novel GNN-based model for ERC, namely S+PAGE, to better capture the speaker and position-aware conversation structure information. Specifically, we add the relative positional encoding and speaker dependency encoding in the representations of edge weights and edge types respectively to acquire a more reasonable aggregation algorithm for ERC. Besides, a two-stream conversational Transformer is presented to extract both the self and inter-speaker contextual features for each utterance. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison, whose results demonstrate the superiority of our model.",
}
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%0 Conference Proceedings
%T S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation
%A Liang, Chen
%A Xu, Jing
%A Lin, Yangkun
%A Yang, Chong
%A Wang, Yongliang
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F liang-etal-2022-page
%X Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. With the development of graph neural network (GNN), recent state-of-the-art ERC models mostly use GNN to embed the intrinsic structure information of a conversation into the utterance features. In this paper, we propose a novel GNN-based model for ERC, namely S+PAGE, to better capture the speaker and position-aware conversation structure information. Specifically, we add the relative positional encoding and speaker dependency encoding in the representations of edge weights and edge types respectively to acquire a more reasonable aggregation algorithm for ERC. Besides, a two-stream conversational Transformer is presented to extract both the self and inter-speaker contextual features for each utterance. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison, whose results demonstrate the superiority of our model.
%U https://aclanthology.org/2022.aacl-main.12
%P 148-157
Markdown (Informal)
[S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation](https://aclanthology.org/2022.aacl-main.12) (Liang et al., AACL-IJCNLP 2022)
ACL