Improving negation detection with negation-focused pre-training

• URL: http://arxiv.org/abs/2205.04012v1
• Date: Mon, 9 May 2022 02:41:11 GMT
• Title: Improving negation detection with negation-focused pre-training
• Authors: Thinh Hung Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor
• Abstract summary: Negation is a common linguistic feature that is crucial in many language understanding tasks. Recent work has shown that state-of-the-art NLP models underperform on samples containing negation. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking.
• Score: 58.32362243122714
• License: http://creativecommons.org/licenses/by/4.0/
• 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 work has shown 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 (Khandewal and Sawant, 2020).

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