Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

Rui Xia, Zixiang Ding


Abstract
Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our approach.
Anthology ID:
P19-1096
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1003–1012
Language:
URL:
https://aclanthology.org/P19-1096
DOI:
10.18653/v1/P19-1096
Award:
 Outstanding Paper
Bibkey:
Cite (ACL):
Rui Xia and Zixiang Ding. 2019. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1003–1012, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (Xia & Ding, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1096.pdf
Code
 NUSTM/ECPE +  additional community code
Data
Xia and Ding, 2019