GraSp: Grammar Learning from Unlabelled Speech Corpora

GraSp: Grammar learning from unlabelled speech corpora Peter Juel Henrichsen CMOL Center for Computational Modelling of Language c/o Dept. of Computational Linguistics Copenhagen Business School Frederiksberg, Denmark pjuel@id.cbs.dk Abstract This paper presents the ongoing project Computational Models of First Language Acquisition, together with its current product, the learning algorithm GraSp. GraSp is designed specifically for inducing grammars from large, unlabelled corpora of spontaneous (i.e. unscripted) speech. The learning algorithm does not assume a predefined grammatical taxonomy; rather the determination of categories and their relations is considered as part of the learning task. While GraSp learning can be used for a range of practical tasks, the long-term goal of the project is to contribute to the debate of innate linguistic knowledge – under the hypothesis that there is no such. Introduction Most current models of grammar learning assume a set of primitive linguistic categories and constraints, the learning process being modelled as category filling and rule instantiation – rather than category formation and rule creation. Arguably, distributing linguistic data over predefined categories and templates does not qualify as grammar 'learning' in the strictest sense, but is better described as 'adjustment' or 'adaptation'. Indeed, Chomsky, the prime advocate of the hypothesis of innate linguistic principles, has claimed that "in certain fundamental respects we do not really learn language" (Chomsky 1980: 134). As Chomsky points out, the complexity of the learning task is greatly reduced given a structure of primitive linguistic constraints ("a highly restrictive schematism", ibid.). It has however been very hard to establish independently the psychological reality of such a structure, and the question of innateness is still far from settled. While a decisive experiment may never be conceived, the issue could be addressed indirectly, e.g. by asking: Are innate principles and parameters necessary preconditions for grammar acquisition? Or rephrased in the spirit of constructive logic: Can a learning algorithm be devised that learns what the infant learns without incorporating specific linguistic axioms? The presentation of such an algorithm would certainly undermine arguments referring to the 'poverty of the stimulus', showing the innateness hypothesis to be dispensable. This paper presents our first try. 1 The essential algorithm 1.1 Psycho-linguistic preconditions Typical spontaneous speech is anything but syntactically 'well-formed' in the Chomskyan sense of the word. right well let's er --= let's look at the applications - erm - let me just ask initially this -- I discussed it with er Reith er but we'll = have to go into it a bit further - is it is it within our erm er = are we free er to er draw up a rather = exiguous list - of people to interview (sample from the London-Lund corpus) Yet informal speech is not perceived as being disorderly (certainly not by the language learning infant), suggesting that its organizing principles differ from those of the written language. So, arguably, a speech grammar inducing algorithm should avoid referring to the usual categories of text based linguistics – 'sentence', 'determiner phrase', etc.1 Instead we allow a …

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