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    <abstract>When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize 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 a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.</abstract>
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%0 Conference Proceedings
%T RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
%A Wang, Bailin
%A Shin, Richard
%A Liu, Xiaodong
%A Polozov, Oleksandr
%A Richardson, Matthew
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-rat
%X When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize 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 a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.
%R 10.18653/v1/2020.acl-main.677
%U https://aclanthology.org/2020.acl-main.677
%U https://doi.org/10.18653/v1/2020.acl-main.677
%P 7567-7578

Markdown (Informal)

[RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://aclanthology.org/2020.acl-main.677) (Wang et al., ACL 2020)

• RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (Wang et al., ACL 2020)

ACL

• Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020. RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7567–7578, Online. Association for Computational Linguistics.