Sequential Labeling for Tracking Dynamic Dialog States

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Sequential Labeling for Tracking Dynamic Dialog States
Title:
Sequential Labeling for Tracking Dynamic Dialog States
Journal Title:
Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL)
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Publication Date:
18 June 2014
Citation:
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.
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