Using Computational Models to Test Syntactic Learnability
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ling_a_00491.pdf
Description
Published version
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9.96 MB
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Adobe PDF
Checksum (MD5)
51e7cb579014e40696dd6312b1ff3b0a
Author(s)
Wilcox, Ethan Gotlieb
Futrell, Richard
Levy, Roger
Date Issued
2022
Journal
Linguistic Inquiry
Publisher
MIT Press
Citation
Wilcox, Ethan Gotlieb, Futrell, Richard and Levy, Roger. 2022. "Using Computational Models to Test Syntactic Learnability." Linguistic Inquiry.
Version
Final published version
Abstract
Abstract
We study the learnability of English filler–gap dependencies and the “island” constraints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep learning to predict the next word given preceding context. Using factorial tests inspired by experimental psycholinguistics, we find that models acquire not only the basic contingency between fillers and gaps, but also the unboundedness and hierarchical constraints implicated in the dependency. We evaluate a model’s acquisition of island constraints by demonstrating that its expectation for a filler–gap contingency is attenuated within an island environment. Our results provide empirical evidence against the Argument from the Poverty of the Stimulus for this particular structure.
We study the learnability of English filler–gap dependencies and the “island” constraints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep learning to predict the next word given preceding context. Using factorial tests inspired by experimental psycholinguistics, we find that models acquire not only the basic contingency between fillers and gaps, but also the unboundedness and hierarchical constraints implicated in the dependency. We evaluate a model’s acquisition of island constraints by demonstrating that its expectation for a filler–gap contingency is attenuated within an island environment. Our results provide empirical evidence against the Argument from the Poverty of the Stimulus for this particular structure.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/LING_A_00491
