Modeling garden path effects without explicit hierarchical syntax

Marten van Schijndel, Tal Linzen

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

    Abstract

    The disambiguation of syntactically ambiguous sentences can lead to reading difficulty, often referred to as a garden path effect. The surprisal hypothesis suggests that this difficulty can be accounted for using word predictability. We tested this hypothesis using predictability estimates derived from two families of language models: grammar-based models, which explicitly encode the syntax of the language; and recurrent neural network (RNN) models, which do not. Both classes of models correctly predicted increased difficulty in ambiguous sentences compared to controls, suggesting that the syntactic representations induced by RNNs are sufficient for this purpose. At the same time, surprisal estimates derived from all models systematically underestimated the magnitude of the effect, and failed to predict the difference between easier (NP/S) and harder (NP/Z) ambiguities. This suggests that it may not be possible to reduce garden path effects to predictability.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
    PublisherThe Cognitive Science Society
    Pages2603-2608
    Number of pages6
    ISBN (Electronic)9780991196784
    StatePublished - 2018
    Event40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 - Madison, United States
    Duration: Jul 25 2018Jul 28 2018

    Publication series

    NameProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018

    Conference

    Conference40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
    Country/TerritoryUnited States
    CityMadison
    Period7/25/187/28/18

    Keywords

    • garden path
    • neural networks
    • self-paced reading

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Human-Computer Interaction
    • Cognitive Neuroscience

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