@inproceedings{hossain-etal-2022-question,
title = "A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations",
author = "Hossain, Md Mosharaf and
Holman, Luke and
Kakileti, Anusha and
Kao, Tiffany and
Brito, Nathan and
Mathews, Aaron and
Blanco, Eduardo",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.37",
doi = "10.18653/v1/2022.findings-naacl.37",
pages = "490--503",
abstract = "This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1{\%} of them convey that an event actually occurred. Annotators generate and answer 7,277 questions {\%} converted for 4,000 for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings substantial improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.",
}
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%0 Conference Proceedings
%T A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations
%A Hossain, Md Mosharaf
%A Holman, Luke
%A Kakileti, Anusha
%A Kao, Tiffany
%A Brito, Nathan
%A Mathews, Aaron
%A Blanco, Eduardo
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F hossain-etal-2022-question
%X This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1% of them convey that an event actually occurred. Annotators generate and answer 7,277 questions % converted for 4,000 for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings substantial improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.
%R 10.18653/v1/2022.findings-naacl.37
%U https://aclanthology.org/2022.findings-naacl.37
%U https://doi.org/10.18653/v1/2022.findings-naacl.37
%P 490-503
Markdown (Informal)
[A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations](https://aclanthology.org/2022.findings-naacl.37) (Hossain et al., Findings 2022)
ACL