Computational Models of First Language Acquisition Special Issue of Research on Language and Computation

Abstract

How children acquire language has stood, for a long time, as one of the most fundamental, beguiling, and surprisingly open questions of modern science. Recent advances in natural language processing, statistical parsing and machine learning, together with the availability of large corpora of child directed speech and other corpora, make a wide range of computationally-oriented approaches to the study of this problem available. Given a model of some aspect of language acquisition, implementing it as a computational system and evaluating it on naturally occurring corpora has a number of compelling advantages. First of all by implementing the system, we can be sure that the algorithm is fully specified, and the acquisition model does not resort to hand-waving at crucial points. Secondly, by evaluating it on real linguistic data, we can see whether naturally occurring distributions of examples in corpora provide sufficient information to support the studied claims across a divergent range of acquisition theories. Thirdly, study of the system can identify the mechanisms that cause changes in the algorithm’s hypotheses during the course of acquisition. Finally, the computational resources required of the model can be concretely assessed and (not so concretely) compared against the resources that might be available to a human language learner.