Sequential labeling for tracking dynamic dialog states

Abstract

This paper presents a sequential labeling approach for tracking the dialog states for the cases of goal changes in a dialog session. The tracking models are trained using linear-chain conditional random fields with the features obtained from the results of SLU. The experimental results show that our proposed approach can improve the performances of the sub-tasks of the second dialog state tracking challenge.