Unknown example detection for example-based spoken dialog system

Abstract

In a spoken dialog system, the example-based response generation method generates a response by searching a dialog example database for the example question most similar to an input user utterance. That method has the advantage of ease of system expansion. It requires, however, a number of utterance examples whose correct responses are labeled. In this paper, we propose an approach to reducing the system expansion cost. This approach employs a detection method that screens the unknown examples, the utterances to be added to the database with their correct responses. The experimental results show that the method can reduce the number of utterances required to be labeled while maintaining the system response accuracy improvement as well as full labeling.