TY - CONF
T1 - Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding
AU - Hossain, Md Mosharaf
AU - Blanco, Eduardo
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1845757. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Computational resources were provided by the UNT office of High-Performance Computing. Further, we leveraged computational resources from the Chameleon platform (Keahey et al., 2020). We also thank the reviewers for insightful comments.
Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85149442244&partnerID=8YFLogxK
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M3 - Paper
AN - SCOPUS:85149442244
SP - 5833
EP - 5847
T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Y2 - 7 December 2022 through 11 December 2022
ER -