Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition Lifeng Jin The Ohio State University Department of Linguistics jin.544@osu.edu Lane Schwartz University of Illinois at Urbana-Champaign Department of Linguistics lanes@illinois.edu Finale Doshi-Velez Harvard University Department of Computer Science finale@seas.harvard.edu Timothy Miller Boston Children’s Hospital & Harvard Medical School Computational Health Informatics Program timothy.miller@childrens.harvard.edu William Schuler∗ The Ohio State University Department of Linguistics schuler@ling.osu.edu This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory capacity, where the shallow grammars are constrained images of the Submission received: 4 March 2020; revised version received: 18 December 2020; accepted for publication: 20 December 2020. https://doi.org/10.1162/COLI a 00399 © 2021 Association for Computational Linguistics Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license Computational Linguistics Volume 47, Number 1 recursive grammars. An implementation of these memory bounds as limits on center embedding in a depth-specific transform of a recursive grammar yields a significant improvement over an equivalent but unbounded baseline, suggesting that this arrangement may indeed confer a learning advantage. 1. Introduction Chomsky (1965) postulates that as human children are naturally exposed to a language, the quantity and nature of the linguistic examples to which they are exposed is in- sufficient to fully explain the children’s successful acquisition of the grammar of the language; Chomsky (1980) dubs this claim the poverty of the stimulus. Chomsky (1965) asserts that the space of possible human languages must therefore be constrained by a set of linguistic universals with which children’s brains are innately primed, and that this biological fact is a necessary precondition for human language learning. Chomsky (1986) uses the term Universal Grammar to describe this proposed innate mental model that underlies human language acquisition. The argument from the poverty of the stimulus and the associated claim of an innate Universal Grammar gained wide acceptance within the Chomskyan generative tradition. The specific details of exactly what aspects of language cannot be learned without Universal Grammar has not always been well defined; similarly, the nature of exactly what proposed linguistic universals constitute Universal Grammar have been widely debated. In striving to identify empirical mechanisms by which poverty of the stimulus claims might be rigorously tested, Pullum and Scholz (2002) conclude that although such claims could potentially be true, the linguistic examples most widely cited in support fail to hold up to close scrutiny. Pullum and Scholz argue that mathematical learning theory and corpus linguistics have a key role to play in empirically testing poverty of the stimulus claims. Preliminary work along these lines using manually constructed grammars of child-directed speech was performed by Perfors, Tenenbaum, and Regier (2006), who …

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