Markovian discriminative modeling for cross-domain dialog state tracking
Hang Ren, Weiqun Xu, Yonghong Yan · 2014
Dialog state tracking (DST), which infers user goals in the presence of noise, is important for spoken dialog systems. Recently it has attracted a lot of attention in the dialog research community. Several new tracking approaches have been proposed, especially in the series of DST Challenges (DSTC). But the problem of cross-domain generalization, i.e., whether trackers designed for one domain will perform similarly well on other domains, is still an open issue. This becomes the focus in DSTC3. To tackle this problem, we adopt domain-independent models and features. We extend our Markovian discriminative model with a joint feature space for effective parameter sharing, so as to accommodate the domain mismatch. In addition, a new two-step training procedure is used to mitigate the `label over-coupling' problem brought by the Markovian structure. When evaluated on the DSTC3 data, our system outperforms all the baselines.