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We focus on the cross-domain context-dependent text-to-SQL generation task.Based on the observation that adjacent natural language questions are oftenlinguistically dependent and their corresponding SQL queries tend to overlap,we utilize the interaction history by editing the previous predicted query toimprove the generation quality. Our editing mechanism views SQL as sequencesand reuses generation results at the token level in a simple manner. It isflexible to change individual tokens and robust to error propagation.Furthermore, to deal with complex table structures in different domains, weemploy an utterance-table encoder and a table-aware decoder to incorporate thecontext of the user utterance and the table schema. We evaluate our approach onthe SParC dataset and demonstrate the benefit of editing compared with thestate-of-the-art baselines which generate SQL from scratch. Our code isavailable at https://github.com/ryanzhumich/sparc_atis_pytorch.

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions