Syntactic Islands and Universal Grammar: A computational model of the acquisition of constraints on long-distance dependencies
Lisa Pearl, Jon Sprouse
August 2012

The induction problems facing language learners have played a central role in debates about the types of learning biases that exist in the human brain. Many linguists have argued that the necessary learning biases to solve these language induction problems must be both innate and language-specific (i.e., the Universal Grammar (UG) hypothesis). Though there have been several recent high-profile investigations of the necessary types of learning biases, the UG hypothesis is still the dominant assumption for a large segment of linguists due to the lack of studies addressing central phenomena in generative linguistics. To address this, we focus on how to learn constraints on long-distance dependencies, also known as syntactic island constraints. We use formal acceptability judgment data to identify the target state of learning for syntactic island constraints, and conduct a corpus analysis of child-directed data to affirm that there does appear to be an induction problem when learning these constraints. We then create a computational learning model that successfully learns the pattern of acceptability judgments observed in formal experiments, based on realistic input data. We then discuss the learning biases required by this model to determine if any must clearly be innate and domain-specific. We find that only one of the proposed biases could potentially be innate and domain-specific, though it could also plausibly be learned. We discuss questions raised by the nature of the linguistic knowledge that is required by this learner, as well as the consequences of this learner for the learning bias debates.
Format: [ pdf ]
Reference: lingbuzz/001493
(please use that when you cite this article)
keywords: universal grammar, syntactic islands, statistical learning, computational modeling, child-directed speech, learnability, syntax
previous versions: v2 [July 2012]
v1 [April 2012]
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