What do you learn from context? Probing for sentence structure in contextualized word representations

Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick

    Research output: Contribution to conferencePaper

    Abstract

    Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.

    Original languageEnglish (US)
    StatePublished - Jan 1 2019
    Event7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States
    Duration: May 6 2019May 9 2019

    Conference

    Conference7th International Conference on Learning Representations, ICLR 2019
    CountryUnited States
    CityNew Orleans
    Period5/6/195/9/19

    ASJC Scopus subject areas

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

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  • Cite this

    Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., Thomas McCoy, R., Kim, N., Van Durme, B., Bowman, S. R., Das, D., & Pavlick, E. (2019). What do you learn from context? Probing for sentence structure in contextualized word representations. Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States.