Skip to content
conftrace_
2024 ACL ACL 2024

Paraphrasing in Affirmative Terms Improves Negation Understanding

AbstractNegation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

More from ACL 2024

All ACL 2024 papers →