# NL-EDIT: Correcting semantic parse errors through natural language interaction

arXiv: https://arxiv.org/abs/2103.14540 · HTML: https://mlsanity.com/paper/2103.14540/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [SParC: Cross-Domain Semantic Parsing in Context](https://mlsanity.com/paper/1906.02285/) (2019)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://mlsanity.com/paper/1909.00786/) (2019)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases](https://mlsanity.com/paper/2004.03125/) (2020)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020)
- [Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study](https://mlsanity.com/paper/1910.05389/) (2019)
- [Learning to Update Natural Language Comments Based on Code Changes](https://mlsanity.com/paper/2004.12169/) (2020)
- [AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data](https://mlsanity.com/paper/2010.04806/) (2020)
- [Learning to Map Context-Dependent Sentences to Executable Formal Queries](https://mlsanity.com/paper/1804.06868/) (2018)
- [DuoRAT: Towards Simpler Text-to-SQL Models](https://mlsanity.com/paper/2010.11119/) (2020)
- [Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers](https://mlsanity.com/paper/1906.11790/) (2019)
- [Building a Neural Semantic Parser from a Domain Ontology](https://mlsanity.com/paper/1812.10037/) (2018)

## Built on by (most cited first, 30 of 33)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [REFINER: Reasoning Feedback on Intermediate Representations](https://mlsanity.com/paper/2304.01904/) (2023)
- [Constrained Language Models Yield Few-Shot Semantic Parsers](https://mlsanity.com/paper/2104.08768/) (2021)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)
- [Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://mlsanity.com/paper/2305.00955/) (2023)
- [Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data](https://mlsanity.com/paper/2106.05006/) (2021)
- [A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning](https://mlsanity.com/paper/2501.02497/) (2025)
- [Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback](https://mlsanity.com/paper/2112.09737/) (2021)
- [Benchmarking and Improving Text-to-SQL Generation under Ambiguity](https://mlsanity.com/paper/2310.13659/) (2023)
- [CodeAgent: Autonomous Communicative Agents for Code Review](https://mlsanity.com/paper/2402.02172/) (2024)
- [Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback](https://mlsanity.com/paper/2304.10750/) (2023)
- [Text-to-SQL Error Correction with Language Models of Code](https://mlsanity.com/paper/2305.13073/) (2023)
- [When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels](https://mlsanity.com/paper/2210.15893/) (2022)
- [Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations](https://mlsanity.com/paper/2305.07372/) (2023)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [SCREWS: A Modular Framework for Reasoning with Revisions](https://mlsanity.com/paper/2309.13075/) (2023)
- [THOUGHTSCULPT: Reasoning with Intermediate Revision and Search](https://mlsanity.com/paper/2404.05966/) (2024)
- [SQLucid: Grounding Natural Language Database Queries with Interactive Explanations](https://mlsanity.com/paper/2409.06178/) (2024)
- [Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction](https://mlsanity.com/paper/2110.08345/) (2021)
- [Interscript: A dataset for interactive learning of scripts through error feedback](https://mlsanity.com/paper/2112.07867/) (2021)
- [Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering](https://mlsanity.com/paper/2306.06779/) (2023)
- [Improving Neural Model Performance through Natural Language Feedback on Their Explanations](https://mlsanity.com/paper/2104.08765/) (2021)
- [A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples](https://mlsanity.com/paper/2404.15974/) (2024)
- [Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models](https://mlsanity.com/paper/2501.02026/) (2025)
- [Can LLMs Patch Security Issues?](https://mlsanity.com/paper/2312.00024/) (2023)
- [Memory-assisted prompt editing to improve GPT-3 after deployment](https://mlsanity.com/paper/2201.06009/) (2022)
- [Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding](https://mlsanity.com/paper/2305.19974/) (2023)
- [What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception](https://mlsanity.com/paper/2311.09558/) (2023)
- [Text Editing as Imitation Game](https://mlsanity.com/paper/2210.12276/) (2022)
- [GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL](https://mlsanity.com/paper/2509.12612/) (2025)

## Abstract (arXiv)

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.