Statistical dialog management applied to WFST-based dialog systems

We have proposed an expandable dialog scenario description and platform to manage dialog systems using a weighted finite-state transducer (WFST) in which user concept and system action tags are input and output of the transducer, respectively. In this paper, we apply this framework to statistical dialog management in which a dialog strategy is acquired from a corpus of human-to-human conversation for hotel reservation. A scenario WFST for dialog management was automatically created from an N-gram model of a tag sequence that was annotated in the corpus with Interchange Format (IF). Additionally, a word-to-concept WFST for spoken language understanding (SLU) was obtained from the same corpus. The acquired scenario WFST and SLU WFST were composed together and then optimized. We evaluated the proposed WFST-based statistic dialog management in terms of correctness to detect the next system actions and have confirmed the automatically acquired dialog scenario from a corpus can manage dialog reasonably on the WFST-based dialog management platform.