Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme

Chaofa Yuan, Chuang Fan, Jianzhu Bao, Ruifeng Xu


Abstract
The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously. Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up. Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p<0.001) in F1 measure.
Anthology ID:
2020.emnlp-main.289
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3568–3573
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.289
DOI:
10.18653/v1/2020.emnlp-main.289
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.289.pdf