Modeling Child Divergences from Adult Grammar

Transactions of the Association for Computational Linguistics, 1 (2013) 125–138. Action Editor: Sharon Goldwater. Submitted 10/2012; Revised 3/2013; Published 5/2013. c⃝2013 Association for Computational Linguistics. Modeling Child Divergences from Adult Grammar Sam Sahakian University of Wisconsin-Madison sahakian@cs.wisc.edu Benjamin Snyder University of Wisconsin-Madison bsnyder@cs.wisc.edu Abstract During the course of first language acquisi- tion, children produce linguistic forms that do not conform to adult grammar. In this paper, we introduce a data set and approach for sys- tematically modeling this child-adult grammar divergence. Our corpus consists of child sen- tences with corrected adult forms. We bridge the gap between these forms with a discrim- inatively reranked noisy channel model that translates child sentences into equivalent adult utterances. Our method outperforms MT and ESL baselines, reducing child error by 20%. Our model allows us to chart specific aspects of grammar development in longitudinal stud- ies of children, and investigate the hypothesis that children share a common developmental path in language acquisition. 1 Introduction Since the publication of the Brown Study (1973), the existence of standard stages of development has been an underlying assumption in the study of first language learning. As a child moves towards lan- guage mastery, their language use grows predictably to include more complex syntactic structures, even- tually converging to full adult usage. In the course of this process, children may produce linguistic forms that do not conform to the grammatical standard. From the adult point of view these are language er- rors, a label which implies a faulty production. Con- sidering the work-in-progress nature of a child lan- guage learner, these divergences could also be de- scribed as expressions of the structural differences between child and adult grammar. The predictability of these divergences has been observed by psychol- ogists, linguists and parents (Owens, 2008).1 Our work leverages the differences between child and adult language to make two contributions to- wards the study of language acquisition. First, we provide a corpus of errorful child sentences anno- tated with adult-like rephrasings. This data will al- low researchers to test hypotheses and build models relating the development of child language to adult forms. Our second contribution is a probabilistic model trained on our corpus that predicts a gram- matical rephrasing given an errorful child sentence. The generative assumption of our model is that sentences begin in underlying adult forms, and are then stochastically transformed into observed child utterances. Given an observed child utterance s, we calculate the probability of the corrected adult trans- lation t as P(t|s) ∝ P(s|t)P(t), where P(t) is an adult language model and P(s|t) is a noise model crafted to capture child grammar errors like omission of certain function words and corruptions of tense or declension. The parame- ters of this noise model are estimated using our cor- pus of child and adult-form utterances, using EM to handle unobserved word alignments. We use this generative model to produce n-best lists of candi- date corrections which are then reranked using long range sentence features in a discriminative frame- work …

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