# Mention Extraction and Linking for SQL Query Generation

arXiv: https://arxiv.org/abs/2012.10074 · HTML: https://mlsanity.com/paper/2012.10074/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://mlsanity.com/paper/2208.13629/) (2022)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [Reliable Text-to-SQL with Adaptive Abstention](https://mlsanity.com/paper/2501.10858/) (2025)
- [Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database](https://mlsanity.com/paper/2211.05165/) (2022)
- [MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction](https://mlsanity.com/paper/2509.23459/) (2025)
- [Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding](https://mlsanity.com/paper/2209.14415/) (2022)
- [Translating Natural Language Queries to SQL Using the T5 Model](https://mlsanity.com/paper/2312.12414/) (2023)

## Abstract (arXiv)

On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex butalso of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.