Incorporating Dialogue Context and Topic Clustering in Out-of-Domain Detection
Ian Richard Lane, Tatsuya Kawahara · 2006
The detection and handling of OOD (out-of-domain) user utterances are significant problems for spoken language systems. We have proposed a novel OOD detection framework, which makes use of classification confidence scores of multiple topics. In this paper, we extend this framework in order to handle natural language dialogue. Specifically, two issues are addressed. First, to effectively incorporate dialogue context, we investigate methods to combine multiple utterances at various stages of the OOD detection process. Second, to improve robustness on spontaneous speech, we introduce a topic clustering scheme which provides reliable topic classification confidence even for indistinct utterances. The system is evaluated on natural dialogue via the ATR speech-to-speech translation system, and a significant improvement in OOD detection accuracy was achieved by incorporating the two proposed techniques.