  The task of context-dependent text-to-SQL aims to convert multi-turn user
utterances to formal SQL queries. This is a challenging task due to both the
scarcity of training data from which to learn complex contextual dependencies
and to generalize to unseen databases. In this paper we explore augmenting the
training datasets using self-play, which leverages contextual information to
synthesize new interactions to adapt the model to new databases. We first
design a SQL-to-text model conditioned on a sampled goal query, which
represents a user's intent, that then converses with a text-to-SQL semantic
parser to generate new interactions. We then filter the synthesized
interactions and retrain the models with the augmented data. We find that
self-play improves the accuracy of a strong baseline on SParC and CoSQL, two
widely used cross-domain text-to-SQL datasets. Our analysis shows that
self-play simulates various conversational thematic relations, enhances
cross-domain generalization and improves beam-search.
