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In this paper, we propose a novel SQL guided pre-training framework STAR forcontext-dependent text-to-SQL parsing, which leverages contextual informationto enrich natural language (NL) utterance and table schema representations fortext-to-SQL conversations. Concretely, we propose two novel pre-trainingobjectives which respectively explore the context-dependent interactions of NLutterances and SQL queries within each text-to-SQL conversation: (i) schemastate tracking (SST) objective that tracks and explores the schema states ofcontext-dependent SQL queries in the form of schema-states by predicting andupdating the value of each schema slot during interaction; (ii) utterancedependency tracking (UDT) objective that employs weighted contrastive learningto pull together two semantically similar NL utterances and push away therepresentations of semantically dissimilar NL utterances within eachconversation. In addition, we construct a high-quality large-scalecontext-dependent text-to-SQL conversation corpus to pre-train STAR. Extensiveexperiments show that STAR achieves new state-of-the-art performance on twodownstream benchmarks (SParC and CoSQL), significantly outperforming previouspre-training methods and ranking first on the leaderboard. We believe therelease of the constructed corpus, codebase and pre-trained STAR checkpointswould push forward the research in this area. For reproducibility, we releaseour code and data athttps://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star.

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing