Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding

Md Mosharaf Hossain, Eduardo Blanco

Abstract
Negation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks. We present an automated procedure to collect pairs of sentences with negation and their affirmative interpretations, resulting in over 150,000 pairs. Experimental results show that leveraging these pairs helps (a) T5 generate affirmative interpretations from negations in a previous benchmark, and (b) a RoBERTa-based classifier solve the task of natural language inference. We also leverage our pairs to build a plug-and-play neural generator that given a negated statement generates an affirmative interpretation. Then, we incorporate the pretrained generator into a RoBERTa-based classifier for sentiment analysis and show that doing so improves the results. Crucially, our proposal does not require any manual effort.
Anthology ID:
2022.emnlp-main.393
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5833–5847
Language:
URL:
https://aclanthology.org/2022.emnlp-main.393
DOI:
10.18653/v1/2022.emnlp-main.393
Bibkey:
Cite (ACL):
Md Mosharaf Hossain and Eduardo Blanco. 2022. Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5833–5847, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding (Hossain & Blanco, EMNLP 2022)
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