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Scalability for handling unknown slot values is a important problem indialogue state tracking (DST). As far as we know, previous scalable DSTapproaches generally rely on either the candidate generation from slot taggingoutput or the span extraction in dialogue context. However, the candidategeneration based DST often suffers from error propagation due to its pipelinedtwo-stage process; meanwhile span extraction based DST has the risk ofgenerating invalid spans in the lack of semantic constraints between start andend position pointers. To tackle the above drawbacks, in this paper, we proposea novel scalable dialogue state tracking method based on slot taggingnavigation, which implements an end-to-end single-step pointer to locate andextract slot value quickly and accurately by the joint learning of slot taggingand slot value position prediction in the dialogue context, especially forunknown slot values. Extensive experiments over several benchmark datasets showthat the proposed model performs better than state-of-the-art baselinesgreatly.

STN4DST: A Scalable Dialogue State Tracking based on Slot Tagging Navigation