  Tracking dialogue states to better interpret user goals and feed downstream
policy learning is a bottleneck in dialogue management. Common practice has
been to treat it as a problem of classifying dialogue content into a set of
pre-defined slot-value pairs, or generating values for different slots given
the dialogue history. Both have limitations on considering dependencies that
occur on dialogues, and are lacking of reasoning capabilities. This paper
proposes to track dialogue states gradually with reasoning over dialogue turns
with the help of the back-end data. Empirical results demonstrate that our
method significantly outperforms the state-of-the-art methods by 38.6% in terms
of joint belief accuracy for MultiWOZ 2.1, a large-scale human-human dialogue
dataset across multiple domains.
