Discriminative Estimation of Interpolation Parameters for Language Model Classifiers

In this paper we present a new approach for estimating the interpolation parameters of language models (LM) which are used as classifiers. With the classical maximum likelihood (ML) est imation theoretically one needs to have a huge amount of data and the fundamental density assumption has to be correct. Usually one of these conditions is violated, so different optimization techniques like maximum mutual information (MMI) and minimum classification error (MCE) can be used instead, where the interpolation parameters are not optimized on its own but in consideration of all models together. In this paper we present how MCE and MMI techniques can be applied to two different kind of interpolation strategies: the linear interpolation, which is the standard interpolation method and the rational interpolation. We compare ML, MCE and MMI on the German part of the Verbmobil corpus, where we get a reduction of 3% of classification error when discrimina ting between 18 dialog act classes.