Human Temporal Inferences Go Beyond Aspectual Class

Katarzyna Pruś, Mark Steedman, Adam Lopez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Past work in NLP has proposed the task of classifying English verb phrases into situation aspect categories, assuming that these categories play an important role in tasks requiring temporal reasoning. We investigate this assumption by gathering crowd-sourced judgements about aspectual entailments from non-expert, native English participants. The results suggest that aspectual class alone is not sufficient to explain the response patterns of the participants. We propose that looking at scenarios which can feasibly accompany an action description contributes towards a better explanation of the participants' answers. A further experiment using GPT-3.5 shows that its outputs follow different patterns than human answers, suggesting that such conceivable scenarios cannot be fully accounted for in the language alone. We release our dataset to support further research.
Original languageEnglish
Title of host publicationProceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
EditorsYvette Graham, Matthew Purver
Place of PublicationSt. Julian's, Malta
PublisherAssociation for Computational Linguistics
Pages1913-1923
Number of pages11
ISBN (Electronic)979-8-89176-088-2
Publication statusPublished - 1 Mar 2024
EventThe 18th Conference of the European Chapter of the Association for Computational Linguistics - , Malta
Duration: 17 Mar 202422 Mar 2024
Conference number: 18
https://2024.eacl.org/

Conference

ConferenceThe 18th Conference of the European Chapter of the Association for Computational Linguistics
Abbreviated titleEACL 2024
Country/TerritoryMalta
Period17/03/2422/03/24
Internet address

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