End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, Xiaoqiang Zhang


Abstract
Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
Anthology ID:
2020.coling-main.17
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
198–207
Language:
URL:
https://aclanthology.org/2020.coling-main.17
DOI:
10.18653/v1/2020.coling-main.17
Bibkey:
Cite (ACL):
Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, and Xiaoqiang Zhang. 2020. End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network. In Proceedings of the 28th International Conference on Computational Linguistics, pages 198–207, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (Chen et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.17.pdf
Code
 chenying3176/PairGCN_ECPE
Data
ECEXia and Ding, 2019