  Dialogue state tracking is a key part of a task-oriented dialogue system,
which estimates the user's goal at each turn of the dialogue. In this paper, we
propose the Point-Or-Generate Dialogue State Tracker (POGD). POGD solves the
dialogue state tracking task in two perspectives: 1) point out explicitly
expressed slot values from the user's utterance, and 2) generate implicitly
expressed ones based on slot-specific contexts. It also shares parameters
across all slots, which achieves knowledge sharing and gains scalability to
large-scale across-domain dialogues. Moreover, the training process of its
submodules is formulated as a multi-task learning procedure to further promote
its capability of generalization. Experiments show that POGD not only obtains
state-of-the-art results on both WoZ 2.0 and MultiWoZ 2.0 datasets but also has
good generalization on unseen values and new slots.
