  Annotating task-oriented dialogues is notorious for the expensive and
difficult data collection process. Few-shot dialogue state tracking (DST) is a
realistic solution to this problem. In this paper, we hypothesize that dialogue
summaries are essentially unstructured dialogue states; hence, we propose to
reformulate dialogue state tracking as a dialogue summarization problem. To
elaborate, we train a text-to-text language model with synthetic template-based
dialogue summaries, generated by a set of rules from the dialogue states. Then,
the dialogue states can be recovered by inversely applying the summary
generation rules. We empirically show that our method DS2 outperforms previous
works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and
multi-domain settings. Our method also exhibits vast speedup during both
training and inference as it can generate all states at once. Finally, based on
our analysis, we discover that the naturalness of the summary templates plays a
key role for successful training.
