End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning

Zixiang Ding, Rui Xia, Jianfei Yu


Abstract
Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL). The window of multi-label learning is centered on the specified emotion clause or cause clause and slides as their positions move. Finally, CMLL and EMLL are integrated to obtain the final result. We evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ECPE task.
Anthology ID:
2020.emnlp-main.290
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3574–3583
Language:
URL:
https://aclanthology.org/2020.emnlp-main.290
DOI:
10.18653/v1/2020.emnlp-main.290
Bibkey:
Cite (ACL):
Zixiang Ding, Rui Xia, and Jianfei Yu. 2020. End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3574–3583, Online. Association for Computational Linguistics.
Cite (Informal):
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (Ding et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.290.pdf
Video:
 https://slideslive.com/38939373
Code
 NUSTM/ECPE-MLL
Data
Xia and Ding, 2019