# Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers

arXiv: https://arxiv.org/abs/1906.11790 · HTML: https://mlsanity.com/paper/1906.11790/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [NL-EDIT: Correcting semantic parse errors through natural language interaction](https://mlsanity.com/paper/2103.14540/) (2021)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey](https://mlsanity.com/paper/2208.04415/) (2022)
- [Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding](https://mlsanity.com/paper/2305.19974/) (2023)

## Abstract (arXiv)

When translating natural language questions into SQL queries to answer questions from a database, we would like our methods to generalize to domains and database schemas outside of the training set. To handle complex questions and database schemas with a neural encoder-decoder paradigm, it is critical to properly encode the schema as part of the input with the question. In this paper, we use relation-aware self-attention within the encoder so that it can reason about how the tables and columns in the provided schema relate to each other and use this information in interpreting the question. We achieve significant gains on the recently-released Spider dataset with 42.94% exact match accuracy, compared to the 18.96% reported in published work.