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Computational Models of Language Acquisition

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Handbook of Generative Approaches to Language Acquisition

Part of the book series: Studies in Theoretical Psycholinguistics ((SITP,volume 41))

Abstract

All models strive to represent reality, and efforts in language research are no ­exception. Computational models of language acquisition must begin and end as an integral part of the empirical study of child language.

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Notes

  1. 1.

    We emphasize at the outset that the exploration of the input data only addresses part of the language acquisition problem. Linguistic studies have revealed many constraints on the syntactic system that are negative in nature, i.e., they specify the impossible forms of language. While theoretical formulations vary, the generalizations of island constraints, binding principles, etc. remain, and the acquisition studies of these constraints have been largely successful; see O’Grady (1997), Crain and Thornton (1998), Guasti (2002), etc. for reviews.

  2. 2.

    For example, given the sentence “the cat chases the mouse”, its bigrams (n  =  2) are “the cat”, “cat chases”, “chases the”, and “the mouse”, and its trigrams (n  =  3) are “the cat chases”, “cat chases the”, “chases the mouse”. When n  =  1, we are just dealing with words, or unigrams.

  3. 3.

    Certain rules have been collapsed together as the Treebank frequently annotates rules involving distinct functional heads as separate rules.

  4. 4.

    We put aside the issue whether (probabilistic) context free grammars are the right representation for natural language; there are well known arguments to the contrary.

  5. 5.

    While this may be empirically true in a given corpus, long sentences can be perfectly grammatical and ought to be part of the learner’s linguistic knowledge – and they are, as any child familiar with The House that Jack built and other nursery rhymes knows well.

  6. 6.

    In an interesting manipulation, Redington et al. explore the idea whether having complete knowledge of one category (e.g., nouns) contributes to the clustering of the other categories. This could correspond to some additional strategy by which the child could arrive at a syntactic category. Perhaps somewhat surprisingly, clustering quality actually diminishes. This suggests that even successful clustering of high-frequency words may not provide a sufficiently good bootstrapping device for the clustering of low frequency words. By contrast, children appear to be capable of using known syntactic categories to determine those of unknown words (Valian and Coulson 1988).

  7. 7.

    Nevertheless, supervised learning has been used extensively in computational modeling of language acquisition, often without commentary on its suitability. For instance, virtually all models in the so-called past tense debate of morphology (e.g., Rumelhart and Mcclelland 1986) and those in the phonological learning of Optimality Theory (e.g., Tesar and Smolensky 2000) assume the learner has simultaneous access to paired input–out forms (e.g., “walk  ®  walked”, “drink  ®  drank”, or /dæm/  ®  [dǽm], as in English vowel nasalization), though clearly the input to the child learner does not arrive in this pre-processed form. Learning the pairing is arguably the most challenging component of learning in these cases; see Chan (2008) for extensive discussion.

  8. 8.

    The OV order parameter, therefore, must be set independently and prior to the V2 parameter by its own cue, e.g., a string where the object is followed by the past participle form of the verb, which indicates its base position before any movement.

  9. 9.

    Certainly, admission that input plays a role in language acquisition – how else would the English learning child learn English and the Chinese learning child learn Chinese – does not mean that the input can account for all aspects of the child’s linguistic knowledge. This point seems obvious, though the persistent failure to even assess the role of the input points to a methodological deficiency in the current practice.

  10. 10.

    We refer the reader to Yang (2002, 2006) and references cited therein for distributional evidence that children’s systematic deviation from the target grammar is best explained by appealing to non-target but UG possible parametric options.

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    Charles Yang

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    Jill de Villiers

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    Tom Roeper

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Yang, C. (2011). Computational Models of Language Acquisition. In: de Villiers, J., Roeper, T. (eds) Handbook of Generative Approaches to Language Acquisition. Studies in Theoretical Psycholinguistics, vol 41. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1688-9_4

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