  Dialogue state tracking is the core part of a spoken dialogue system. It
estimates the beliefs of possible user's goals at every dialogue turn. However,
for most current approaches, it's difficult to scale to large dialogue domains.
They have one or more of following limitations: (a) Some models don't work in
the situation where slot values in ontology changes dynamically; (b) The number
of model parameters is proportional to the number of slots; (c) Some models
extract features based on hand-crafted lexicons. To tackle these challenges, we
propose StateNet, a universal dialogue state tracker. It is independent of the
number of values, shares parameters across all slots, and uses pre-trained word
vectors instead of explicit semantic dictionaries. Our experiments on two
datasets show that our approach not only overcomes the limitations, but also
significantly outperforms the performance of state-of-the-art approaches.
