  An important yet rarely tackled problem in dialogue state tracking (DST) is
scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot
values. We focus on a specific condition, where the ontology is unknown to the
state tracker, but the target slot value (except for none and dontcare),
possibly unseen during training, can be found as word segment in the dialogue
context. Prior approaches often rely on candidate generation from n-gram
enumeration or slot tagger outputs, which can be inefficient or suffer from
error propagation. We propose BERT-DST, an end-to-end dialogue state tracker
which directly extracts slot values from the dialogue context. We use BERT as
dialogue context encoder whose contextualized language representations are
suitable for scalable DST to identify slot values from their semantic context.
Furthermore, we employ encoder parameter sharing across all slots with two
advantages: (1) Number of parameters does not grow linearly with the ontology.
(2) Language representation knowledge can be transferred among slots. Empirical
evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior
work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves
competitive performance on the standard DSTC2 and WOZ 2.0 datasets.
