A neural model of adaptation in reading

Marten van Schijndel, Tal Linzen

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

    Abstract

    It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
    EditorsEllen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
    PublisherAssociation for Computational Linguistics
    Pages4704-4710
    Number of pages7
    ISBN (Electronic)9781948087841
    StatePublished - 2020
    Event2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium
    Duration: Oct 31 2018Nov 4 2018

    Publication series

    NameProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

    Conference

    Conference2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
    CountryBelgium
    CityBrussels
    Period10/31/1811/4/18

    ASJC Scopus subject areas

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

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