Exploring transitivity in neural NLI models through veridicality

Hitomi Yanaka, Koji Mineshima, Kentaro Inui

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

Abstract

Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear. We explore this issue in the domain of natural language inference (NLI), focusing on the transitivity of inference relations, a fundamental property for systematically drawing inferences. A model capturing transitivity can compose basic inference patterns and draw new inferences. We introduce an analysis method using synthetic and naturalistic NLI datasets involving clause-embedding verbs to evaluate whether models can perform transitivity inferences composed of veridical inferences and arbitrary inference types. We find that current NLI models do not perform consistently well on transitivity inference tasks, suggesting that they lack the generalization capacity for drawing composite inferences from provided training examples. The data and code for our analysis are publicly available at https://github.com/verypluming/transitivity.

Original languageEnglish
Title of host publicationEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages920-934
Number of pages15
ISBN (Electronic)9781954085022
Publication statusPublished - 2021
Event16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online
Duration: 2021 Apr 192021 Apr 23

Publication series

NameEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
CityVirtual, Online
Period21/4/1921/4/23

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Linguistics and Language

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