A framework for unsupervised transfer learning and application to dialog decision classification

Abstract

We propose a framework for transfer learning in the unsupervised condition, and show its usefulness in addressing the problem of mismatch in test time dialog state decision classifier, which is presented here as a binary hypothesis problem. We are asked to either accept or reject the ASR output. The framework encompasses a two step process, the first step culminates in the discriminative retraining of the test time classifier using the results of an EM solution to the joint optimization between the original labelled training data and observed unlabelled test data for enhanced test time discrimination of the binary classes. The second step is optimization of the performance of this classifier in a specific operating range. This extends previous results in Bayes error reshaping to the unsupervised condition which favor a particular false alarm operating range. We show a total relative reduction in error rate of up to 15%, 12.5% from the first step, with an additional 2.5% from step 2 along with the added knowledge of the threshold needed to operate at a specific false alarm operating range.