  We present Spider, a large-scale, complex and cross-domain semantic parsing
and text-to-SQL dataset annotated by 11 college students. It consists of 10,181
questions and 5,693 unique complex SQL queries on 200 databases with multiple
tables, covering 138 different domains. We define a new complex and
cross-domain semantic parsing and text-to-SQL task where different complex SQL
queries and databases appear in train and test sets. In this way, the task
requires the model to generalize well to both new SQL queries and new database
schemas. Spider is distinct from most of the previous semantic parsing tasks
because they all use a single database and the exact same programs in the train
set and the test set. We experiment with various state-of-the-art models and
the best model achieves only 12.4% exact matching accuracy on a database split
setting. This shows that Spider presents a strong challenge for future
research. Our dataset and task are publicly available at
https://yale-lily.github.io/spider
