Exploring transitivity in neural NLI models through veridicality

Hitomi Yanaka, Koji Mineshima, Kentaro Inui

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
出版社Association for Computational Linguistics (ACL)
ページ920-934
ページ数15
ISBN(電子版)9781954085022
出版ステータスPublished - 2021
イベント16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online
継続期間: 2021 4 192021 4 23

出版物シリーズ

名前EACL 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

  • ソフトウェア
  • 計算理論と計算数学
  • 言語学および言語

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