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).
BibTeX
@inproceedings{truong-etal-2022-improving,
title = "Improving negation detection with negation-focused pre-training",
author = "Truong, Thinh and
Baldwin, Timothy and
Cohn, Trevor and
Verspoor, Karin",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.309/",
doi = "10.18653/v1/2022.naacl-main.309",
pages = "4188--4193"
}