Abstract

We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train an n-gram model on the basis of a probabilistic context-free grammar.

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Author notes

Faculty of Arts, Humanities Computing, P.O. Box 716, NL-9700 AS Groningen, The Netherlands. markjan@let.rug.nl