Abstract
Abstract: This paper presents two discriminative knowledge-based dialog state trackers and their results on the Dialog State Tracking Challenge (DSTC) 2 and 3 datasets. The first tracker was submitted to the DSTC3 competition and scored second in the joint accuracy.
The second tracker developed after the DSTC3 submission deadline gives even better results on the DSTC2 and DSTC3 datasets. It performs on par with the state of the art machine learning-based trackers while offering better interpretability.
We summarize recent directions in the dialog state tracking (DST) and also discuss possible decomposition of the DST problem. Based on the results of DSTC2 and DSTC3 we analyze suitability of different techniques for each of the DST subproblems.
Results of the trackers highlight the importance of Spoken Language Understanding (SLU) for the last two DSTCs.