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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3568–3573
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.289
- DOI:
- 10.18653/v1/2020.emnlp-main.289
- Cite (ACL):
- Chaofa Yuan, Chuang Fan, Jianzhu Bao, and Ruifeng Xu. 2020. Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3568–3573, Online. Association for Computational Linguistics.
- Cite (Informal):
- Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (Yuan et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.289.pdf