Integrating topic estimation and dialogue history for domain selection in multi-domain spoken dialogue systems

We present a method of robust domain selection against out-of-grammar (OOG) utterances in multi-domain spoken dialogue systems. These utterances cause language-understanding errors because of a limited set of grammar and vocabulary of the systems, and deteriorate the domain selection. This is critical for multi-domain spoken dialogue systems to determine a system's response. We first define a topic as a domain from which the user wants to retrieve information, and estimate it as the user's intention. This topic estimation is enabled by using a large amount of sentences collected from the Web and Latent Semantic Mapping (LSM). The results are reliable even for OOG utterances. We then integrated both the topic estimation results and the dialogue history to construct a robust domain classifier against OOG utterances. The idea of integration is based on the fact that the reliability of the dialogue history is often impeded by language-understanding errors caused by OOG utterances, from which using topic estimation obtains useful information. Experimental results using 2191 utterances showed that our integrated method reduced domain selection errors by 14.3%.