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

Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, Matthew Richardson


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.
Anthology ID:
2020.acl-main.677
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7567–7578
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.677
DOI:
10.18653/v1/2020.acl-main.677
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.677.pdf
Video:
 http://slideslive.com/38929348