  Scalability for handling unknown slot values is a important problem in
dialogue state tracking (DST). As far as we know, previous scalable DST
approaches generally rely on either the candidate generation from slot tagging
output or the span extraction in dialogue context. However, the candidate
generation based DST often suffers from error propagation due to its pipelined
two-stage process; meanwhile span extraction based DST has the risk of
generating invalid spans in the lack of semantic constraints between start and
end position pointers. To tackle the above drawbacks, in this paper, we propose
a novel scalable dialogue state tracking method based on slot tagging
navigation, which implements an end-to-end single-step pointer to locate and
extract slot value quickly and accurately by the joint learning of slot tagging
and slot value position prediction in the dialogue context, especially for
unknown slot values. Extensive experiments over several benchmark datasets show
that the proposed model performs better than state-of-the-art baselines
greatly.
