# Photon: A Robust Cross-Domain Text-to-SQL System

arXiv: https://arxiv.org/abs/2007.15280 · HTML: https://mlsanity.com/paper/2007.15280/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Towards Robustness of Text-to-SQL Models against Synonym Substitution](https://mlsanity.com/paper/2106.01065/) (2021)
- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations](https://mlsanity.com/paper/2306.14321/) (2023)
- [MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers](https://mlsanity.com/paper/2109.00799/) (2021)
- [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://mlsanity.com/paper/2212.09994/) (2022)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [Text-to-SQL Error Correction with Language Models of Code](https://mlsanity.com/paper/2305.13073/) (2023)
- [Database Reasoning Over Text](https://mlsanity.com/paper/2106.01074/) (2021)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge](https://mlsanity.com/paper/2301.01067/) (2023)
- [Error Detection for Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.13683/) (2023)
- [SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL](https://mlsanity.com/paper/2506.04494/) (2025)
- [NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands](https://mlsanity.com/paper/2103.02523/) (2021)
- [Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins](https://mlsanity.com/paper/2106.01501/) (2021)
- [Multimodal Neural Databases](https://mlsanity.com/paper/2305.01447/) (2023)
- [Code Generation from Natural Language with Less Prior and More Monolingual Data](https://mlsanity.com/paper/2101.00259/) (2021)
- [Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL](https://mlsanity.com/paper/2212.08902/) (2022)
- [On the Security Vulnerabilities of Text-to-SQL Models](https://mlsanity.com/paper/2211.15363/) (2022)
- [NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation](https://mlsanity.com/paper/2302.07845/) (2023)
- [Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval](https://mlsanity.com/paper/2504.09824/) (2025)
- [Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface](https://mlsanity.com/paper/2106.04559/) (2021)
- [Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2410.16283/) (2024)
- [Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding](https://mlsanity.com/paper/2209.14415/) (2022)
- [CAPER: Clause-Aligned Process Supervision for Text-to-SQL](https://mlsanity.com/paper/2606.03327/) (2026)

## Abstract (arXiv)

Natural language interfaces to databases (NLIDB) democratize end user access to relational data. Due to fundamental differences between natural language communication and programming, it is common for end users to issue questions that are ambiguous to the system or fall outside the semantic scope of its underlying query language. We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. Photon consists of a strong neural semantic parser (63.2% structure accuracy on the Spider dev benchmark), a human-in-the-loop question corrector, a SQL executor and a response generator. The question corrector is a discriminative neural sequence editor which detects confusion span(s) in the input question and suggests rephrasing until a translatable input is given by the user or a maximum number of iterations are conducted. Experiments on simulated data show that the proposed method effectively improves the robustness of text-to-SQL system against untranslatable user input. The live demo of our system is available at http://naturalsql.com.