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

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task