Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal

Yan Cong, Emmanuele Chersoni, Yu Yin Hsu, Alessandro Lenci

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In psycholinguistics, semantic attraction is a sentence processing phenomenon in which a given argument violates the selectional requirements of a verb, but this violation is not perceived by comprehenders due to its attraction to another noun in the same sentence, which is syntactically unrelated but semantically sound. In our study, we use autoregressive language models to compute the sentence-level and the target phrase-level Surprisal scores of a psycholinguistic dataset on semantic attraction. Our results show that the models are sensitive to semantic attraction, leading to reduced Surprisal scores, although none of them perfectly matches the human behavioral patterns.

Original languageEnglish
Title of host publicationStarSEM 2023 - 12th Joint Conference on Lexical and Computational Semantics, Proceedings of the Conference
EditorsAlexis Palmer, Jose Camacho-Collados
PublisherAssociation for Computational Linguistics (ACL)
Pages141-148
Number of pages8
ISBN (Electronic)9781959429760
Publication statusPublished - Jul 2023
Event12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023 - Toronto, Canada
Duration: 13 Jul 202314 Jul 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023
Country/TerritoryCanada
CityToronto
Period13/07/2314/07/23

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

Fingerprint

Dive into the research topics of 'Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal'. Together they form a unique fingerprint.

Cite this