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When translating natural language questions into SQL queries to answerquestions from a database, contemporary semantic parsing models struggle togeneralize to unseen database schemas. The generalization challenge lies in (a)encoding the database relations in an accessible way for the semantic parser,and (b) modeling alignment between database columns and their mentions in agiven query. We present a unified framework, based on the relation-awareself-attention mechanism, to address schema encoding, schema linking, andfeature representation within a text-to-SQL encoder. On the challenging Spiderdataset this framework boosts the exact match accuracy to 57.2%, surpassing itsbest counterparts by 8.7% absolute improvement. Further augmented with BERT, itachieves the new state-of-the-art performance of 65.6% on the Spiderleaderboard. In addition, we observe qualitative improvements in the model'sunderstanding of schema linking and alignment. Our implementation will beopen-sourced at https://github.com/Microsoft/rat-sql.

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers