COMPREHENSION GRAMMARS GENERATED FROM MACHINE LEARNING OF NATURAL LANGUAGES

We are developing a theory of probabilistic language learning in the c ontext of robotic instruction in elementary assembly actions. We descr ibe the process of machine learning in terms of the various events tha t happen on a given trial, including the crucial association of words with internal representations of their meaning. Of central importance in learning is the generalization from utterances to grammatical forms . Our system derives a comprehension grammar for a superset of a natur al language from pairs of verbal stimuli like Go to the screw! and cor responding internal representations of coerced actions. For the deriva tion of a grammar no knowledge of the language to be learned is assume d but only knowledge of an internal language. We present grammars for English, Chinese, and German generated from a finite sample of about 5 00 commands that are roughly equivalent across the three languages. Al l of the three grammars, which are context-free in form, accept an inf inite set of commands in the given language.