Corpus-Based Optimization of Language Models Derived from Unification Grammars
We describe a technique which makes if feasible to improve the performance of a language model derived from a manually constructed unification grammar, using low-quality untranscribed speech data and a minimum of human annotation. The method is evaluated on a medium-vocabulary spoken language command and control task.