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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.

Sequential Labeling for Tracking Dynamic Dialog States