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.

StateNet 是一种通用的对话状态跟踪器，它独立于值的数量，共享所有插槽的参数，并使用预训练的词向量而不是明确的语义字典来解决当前方法在大型对话域上难以扩展的挑战，并且在两个数据集上的实验显示，我们的方法不仅克服了这些限制，而且还显著优于最先进的方法的性能。