Improving negation detection with negation-focused pre-training

Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor


Abstract
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent works show that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation detection models do not transfer well across domains. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking, to better incorporate negation information into language models. Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT (Khandelwal and Sawant, 2020).
Anthology ID:
2022.naacl-main.309
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4188–4193
Language:
URL:
https://aclanthology.org/2022.naacl-main.309
DOI:
10.18653/v1/2022.naacl-main.309
Bibkey:
Cite (ACL):
Thinh Truong, Timothy Baldwin, Trevor Cohn, and Karin Verspoor. 2022. Improving negation detection with negation-focused pre-training. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4188–4193, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Improving negation detection with negation-focused pre-training (Truong et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.309.pdf