  Carefully-designed schemas describing how to collect and annotate dialog
corpora are a prerequisite towards building task-oriented dialog systems. In
practical applications, manually designing schemas can be error-prone,
laborious, iterative, and slow, especially when the schema is complicated. To
alleviate this expensive and time consuming process, we propose an unsupervised
approach for slot schema induction from unlabeled dialog corpora. Leveraging
in-domain language models and unsupervised parsing structures, our data-driven
approach extracts candidate slots without constraints, followed by
coarse-to-fine clustering to induce slot types. We compare our method against
several strong supervised baselines, and show significant performance
improvement in slot schema induction on MultiWoz and SGD datasets. We also
demonstrate the effectiveness of induced schemas on downstream applications
including dialog state tracking and response generation.
