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

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

TL;DR: State of the art in complex text-to-SQL parsing by combining hard and soft relational reasoning in schema/question encoding.

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 53.7%, compared to 47.4% for the previous state-of-the-art model unaugmented with BERT embeddings. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment.

Keywords: semantic parsing, text-to-sql, self-attention, program synthesis, transformer, representation learning, natural language processing

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1911.04942/code)