Markovian Discriminative Modeling for Dialog State Tracking
Hang Ren, Weiqun Xu, Yonghong Yan · 2014
Discriminative dialog state tracking has become a hot topic in dialog research com-munity recently. Compared to genera-tive approach, it has the advantage of be-ing able to handle arbitrary dependent fea-tures, which is very appealing. In this paper, we present our approach to the DSTC2 challenge. We propose to use dis-criminative Markovian models as a natu-ral enhancement to the stationary discrim-inative models. The Markovian structure allows the incorporation of ‘transitional’ features, which can lead to more effi-ciency and flexibility in tracking user goal changes. Results on the DSTC2 dataset show considerable improvements over the baseline, and the effects of the Markovian dependency is tested empirically. 1