On measuring social biases in sentence encoders

Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger

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

    Abstract

    The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test's assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.

    Original languageEnglish (US)
    Title of host publicationLong and Short Papers
    PublisherAssociation for Computational Linguistics (ACL)
    Pages622-628
    Number of pages7
    ISBN (Electronic)9781950737130
    StatePublished - 2019
    Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
    Duration: Jun 2 2019Jun 7 2019

    Publication series

    NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
    Volume1

    Conference

    Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
    CountryUnited States
    CityMinneapolis
    Period6/2/196/7/19

    ASJC Scopus subject areas

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

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