  Existing approaches to dialogue state tracking rely on pre-defined ontologies
consisting of a set of all possible slot types and values. Though such
approaches exhibit promising performance on single-domain benchmarks, they
suffer from computational complexity that increases proportionally to the
number of pre-defined slots that need tracking. This issue becomes more severe
when it comes to multi-domain dialogues which include larger numbers of slots.
In this paper, we investigate how to approach DST using a generation framework
without the pre-defined ontology list. Given each turn of user utterance and
system response, we directly generate a sequence of belief states by applying a
hierarchical encoder-decoder structure. In this way, the computational
complexity of our model will be a constant regardless of the number of
pre-defined slots. Experiments on both the multi-domain and the single domain
dialogue state tracking dataset show that our model not only scales easily with
the increasing number of pre-defined domains and slots but also reaches the
state-of-the-art performance.
