Learning to Predict Denotational Probabilities For Modeling Entailment

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

Abstract

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.

Original languageEnglish (US)
Title of host publicationLong Papers - Continued
PublisherAssociation for Computational Linguistics (ACL)
Pages721-730
Number of pages10
ISBN (Electronic)9781510838604
DOIs
StatePublished - 2017
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: Apr 3 2017Apr 7 2017

Publication series

Name15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume1

Other

Other15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
CountrySpain
CityValencia
Period4/3/174/7/17

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

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  • Cite this

    Lai, A., & Hockenmaier, J. (2017). Learning to Predict Denotational Probabilities For Modeling Entailment. In Long Papers - Continued (pp. 721-730). (15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1068