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Dialog state tracking (DST) maintains and updates dialog states at each time step as the dialog progresses. It is necessary to include dialog historical information in DST. Previous word-based DST models took historical utterances as a word sequence and used n-grams in the sequence as inputs of models. It suffered from the problem of data sparseness. This paper proposes a cascaded deep neural network...
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