Contextual constraints based on dialogue models in database search task for spoken dialogue systems
Kazunori Komatani, Naoyuki Kanda, Tetsuya Ogata, Hiroshi G. Okuno · 2005
This paper describes the incorporation of contextual information into spoken dialogue systems in the database search task. Appropriatedialoguemodeling is requiredto manageautomatic speech recognition (ASR) errors using dialogue-level information. We define two dialogue models: a model for dialogue flow and a model of structured dialogue history. The model for dialogueflowassumesdialoguesin the databasesearchtaskconsist of only two modes. In the structured dialogue history model, query conditions are maintained as a tree structure, taking into consideration their inputted order. The constraints derived from these models are integrated by using a decision tree learning, so that the system candeterminea dialogueact of the utteranceand whether each content word should be accepted or rejected, even when it contains ASR errors. The experimental result showed that our method could interpret content words better than conventional one without the contextual information. Furthermore, it was also shown that our method was domain-independent because it achieved equivalent accuracy in another domain without any more training.