Syntactic Structure from Deep Learning

Tal Linzen, Marco Baroni

    Research output: Contribution to journalReview articlepeer-review


    Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey representative studies of the syntactic abilities of deep networks and discuss the broader implications that this work has for theoretical linguistics.

    Original languageEnglish (US)
    Pages (from-to)195-212
    Number of pages18
    JournalAnnual Review of Linguistics
    StatePublished - Jan 4 2021


    • deep learning
    • nature versus nurture
    • probing linguistic knowledge
    • syntax

    ASJC Scopus subject areas

    • Language and Linguistics
    • Linguistics and Language


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