TY - GEN
T1 - A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations
AU - Hossain, Md Mosharaf
AU - Holman, Luke
AU - Kakileti, Anusha
AU - Kao, Tiffany Iris
AU - Brito, Nathan Raul
AU - Mathews, Aaron Abraham
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. The Titan Xp used for this research was donated by the NVIDIA Corporation. Computational resources were also provided by the UNT office of High-Performance Computing. Additionally, we utilized computational resources from the Chameleon platform (Keahey et al., 2020). We also thank the anonymous reviewers for their insightful comments.
Publisher Copyright:
© Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.
PY - 2022
Y1 - 2022
N2 - 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 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-ofthe- art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings small 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.
AB - 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 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-ofthe- art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings small 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.
UR - http://www.scopus.com/inward/record.url?scp=85137330165&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137330165&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137330165
T3 - Findings of the Association for Computational Linguistics: NAACL 2022 - Findings
SP - 490
EP - 503
BT - Findings of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2022 Findings of the Association for Computational Linguistics: NAACL 2022
Y2 - 10 July 2022 through 15 July 2022
ER -