Improving negation detection with negation-focused pre-training
This addresses a specific linguistic challenge in NLP that affects tasks requiring nuanced language understanding, but it is incremental as it builds on existing pre-training methods.
The paper tackled the problem of poor negation detection and transfer across domains in NLP by proposing a negation-focused pre-training strategy with data augmentation and masking, resulting in improved performance and generalizability over the NegBERT baseline.
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).