Domain Adaptation with Clustered Language Models

Details Domain Adaptation with Clustered Language Models Domain Adaptation with Clustered Language Models

1997-03-04

arxiv.org/abs/cmp-lg/9703001v1

Computer Science

Computation and Language

preprint - to appear in ICASSP 97

Scientific paper

In this paper, a method of domain adaptation for clustered language models is developed. It is based on a previously developed clustering algorithm, but with a modified optimisation criterion. The results are shown to be slightly superior to the previously published 'Fillup' method, which can be used to adapt standard n-gram models. However, the improvement both methods give compared to models built from scratch on the adaptation data is quite small (less than 11% relative improvement in word error rate). This suggests that both methods are still unsatisfactory from a practical point of view.

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