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Computational Linguistics

Paola Merlo, Editor
June 2005, Vol. 31, No. 2, Pages 173-185
(doi: 10.1162/0891201054223986)
© 2005 Association for Computational Linguistics
A General Technique to Train Language Models on Language Models
Article PDF (185.95 KB)
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.