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Context-dependent text-to-SQL task has drawn much attention in recent years.Previous models on context-dependent text-to-SQL task only concentrate onutilizing historical user inputs. In this work, in addition to using encodersto capture historical information of user inputs, we propose a database schemainteraction graph encoder to utilize historicalal information of databaseschema items. In decoding phase, we introduce a gate mechanism to weigh theimportance of different vocabularies and then make the prediction of SQLtokens. We evaluate our model on the benchmark SParC and CoSQL datasets, whichare two large complex context-dependent cross-domain text-to-SQL datasets. Ourmodel outperforms previous state-of-the-art model by a large margin andachieves new state-of-the-art results on the two datasets. The comparison andablation results demonstrate the efficacy of our model and the usefulness ofthe database schema interaction graph encoder.

IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation