A large annotated corpus for learning natural language inference

Samuel R. Bowman, Gabor Angeli, Christopher Potts, Christopher D. Manning

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

    Abstract

    Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.

    Original languageEnglish (US)
    Title of host publicationConference Proceedings - EMNLP 2015
    Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
    PublisherAssociation for Computational Linguistics (ACL)
    Pages632-642
    Number of pages11
    ISBN (Electronic)9781941643327
    DOIs
    StatePublished - 2015
    EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
    Duration: Sep 17 2015Sep 21 2015

    Publication series

    NameConference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

    Other

    OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2015
    CountryPortugal
    CityLisbon
    Period9/17/159/21/15

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Science Applications
    • Information Systems

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