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Modeling regularization in language acquisition as noise-tolerant grammar selection.

Laurel Perkins1, Tim Hunter1

  • 1Department of Linguistics, University of California, Los Angeles, 3125 Campbell Hall, Los Angeles, CA 90025, United States of America.

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Summary
This summary is machine-generated.

Children learn language by assuming underlying grammar rules are restrictive, even with noisy data. This noisy grammar learner model explains language acquisition better than regularization bias in real-world cases.

Keywords:
Bayesian reasoningComputational modelingGrammarLanguage acquisitionRegularizationSyntax

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Area of Science:

  • Computational linguistics
  • Cognitive science
  • Developmental psychology

Background:

  • Language acquisition involves learning systematic rules from imperfect input.
  • Existing models often assume a

Purpose of the Study:

  • To introduce a computational framework for language acquisition that models children's expectations of restrictive underlying grammar.
  • To evaluate a learner that considers "noise" rules alongside core grammatical rules.

Main Methods:

  • Implemented a Noisy Grammar Learner evaluating composite context-free grammars.
  • Compared the learner to a regularization bias model on artificial and naturalistic language data.

Main Results:

  • Both models explained artificial language learning.
  • Only the Noisy Grammar Learner succeeded on naturalistic case studies of word-order and case-marking acquisition.
  • The learner's architecture naturally incorporated linguistically-motivated expectations.

Conclusions:

  • Learning from messy data may rely on a hypothesis space of restrictive grammatical options.
  • The noisy grammar approach offers a more robust explanation for early syntax acquisition than regularization bias.