Sequential Labeling for Tracking Dynamic Dialog States
Seokhwan Kim, Rafael E. Banchs · 2014
This paper presents a sequential labeling approach for tracking the dialog states for the cases of goal changes in a dialog ses-sion. The tracking models are trained us-ing 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. 1