Abstract
We present SParC, a dataset for cross-domain
Semantic Parsing in Context. It consists of
4,298 coherent question sequences (12k+ individual questions annotated with SQL queries),
obtained from controlled user interactions
with 200 complex databases over 138 domains. We provide an in-depth analysis of
SParC and show that it introduces new challenges compared to existing datasets. SParC
(1) demonstrates complex contextual dependencies, (2) has greater semantic diversity, and
(3) requires generalization to new domains
due to its cross-domain nature and the unseen databases at test time. We experiment
with two state-of-the-art text-to-SQL models adapted to the context-dependent, crossdomain setup. The best model obtains an
exact set match accuracy of 20.2% over all
questions and less than 10% over all interaction sequences, indicating that the crossdomain setting and the contextual phenomena
of the dataset present significant challenges
for future research. The dataset, baselines,
and leaderboard are released at https://
yale-lily.github.io/sparc