The goal of Dialogue State Tracking (DST) is to estimate the current dialogue state given all the preceding conversation. Due to the increased number of state candidates, data sparsity problem is still a major hurdle for multi-domain DST. Existing methods generally choose to predict a value for each possible slot over all domains with quite low efficiency. In this paper, we propose a hierarchical dialogue state tracker which consists of three sequential modules: domain classification, slot detection and value extraction. It predicts domains, slots and values dynamically by given the dialogue history and outputs of the preceding module, which can dramatically improve the model efficiency. Experimental results on MultiWOZ2.1 also show that our approach achieves state-of-the-art joint goal accuracy, and confirm that the hierarchical structure can enhance existing DST models significantly.
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