Mind the Gap: Learning the Surface Forms of Movement Dependencies
Laurel Perkins, Naomi Feldman, Jeffrey Lidz
June 2024

In acquiring a syntax, children must detect evidence for abstract structural dependencies that can be realized in variable ways in the surface forms of sentences. In "What did David fix?", learners must identify a non-local relation between a fronted object of the verb ("what") and the phonologically null “gap” in canonical direct object position after the verb, where it is thematically interpreted. How do learners identify a non-adjacent dependency between an expression and something that has no overt phonological form? We propose that identifying abstract syntactic dependencies requires statistical inference over both overt linguistic material and unsatisfied grammatical expectations: noticing when a predicted argument for a verb is unexpectedly missing may serve as evidence for the gap of an argument movement dependency. We provide computational support for this hypothesis. We develop a learner that uses predicted but unexpectedly missing objects of verbs to identify possible gaps of object movement, and identifies which surface morphosyntactic properties of sentences are correlated with these possible movement gaps. We find that it is in principle possible for a learner using this mechanism to identify the majority of sentences with object movement in child-directed English, and that prior knowledge of which verbs require objects provides an important guide for identifying which surface distributions characterize object movement. This provides a computational account for why verb argument structure knowledge developmentally precedes the acquisition of movement in a language like English. More broadly, these findings illustrate how statistical learning and learning from violated expectations can be combined to novel effect in the domain of language acquisition.
Format: [ pdf ]
Reference: lingbuzz/008171
(please use that when you cite this article)
Published in: under review
keywords: language acquisition, computational modeling, statistical learning, expectation violation, non-adjacent dependencies, movement, argument structure, syntax
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