Using Computational Models to Test Syntactic Learnability
Ethan Wilcox, Richard Futrell, Roger Levy
November 2021
 

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.
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Reference: lingbuzz/006327
(please use that when you cite this article)
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keywords: syntactic islands, neural networks, learnability, deep learning, filler-gap dependency, syntax
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