# HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing

arXiv: https://arxiv.org/abs/2203.07376 · HTML: https://mlsanity.com/paper/2203.07376/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 46 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [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)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [R-Drop: Regularized Dropout for Neural Networks](https://mlsanity.com/paper/2106.14448/) (2021)
- [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)
- [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2012.12627/) (2020)
- [Supervised Multimodal Bitransformers for Classifying Images and Text](https://mlsanity.com/paper/1909.02950/) (2019)
- [Structure-Grounded Pretraining for Text-to-SQL](https://mlsanity.com/paper/2010.12773/) (2020)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://mlsanity.com/paper/2010.12412/) (2020)
- [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)
- [Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://mlsanity.com/paper/1905.06241/) (2019)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training](https://mlsanity.com/paper/2012.10309/) (2020)
- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://mlsanity.com/paper/2002.00652/) (2020)
- [Global Reasoning over Database Structures for Text-to-SQL Parsing](https://mlsanity.com/paper/1908.11214/) (2019)
- [Optimizing Deeper Transformers on Small Datasets](https://mlsanity.com/paper/2012.15355/) (2020)

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

- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://mlsanity.com/paper/2205.06983/) (2022)
- [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)
- [UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](https://mlsanity.com/paper/2203.07781/) (2022)
- [STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2210.11888/) (2022)
- [CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions](https://mlsanity.com/paper/2405.02712/) (2024)
- [CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers](https://mlsanity.com/paper/2205.07686/) (2022)
- [Towards Generalizable and Robust Text-to-SQL Parsing](https://mlsanity.com/paper/2210.12674/) (2022)
- [Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams](https://mlsanity.com/paper/2211.11226/) (2022)
- [Learning from Failures in Multi-Attempt Reinforcement Learning](https://mlsanity.com/paper/2503.04808/) (2025)
- [MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL](https://mlsanity.com/paper/2212.09278/) (2022)
- [Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing](https://mlsanity.com/paper/2310.04801/) (2023)
- [Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models](https://mlsanity.com/paper/2405.06674/) (2024)
- [EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records](https://mlsanity.com/paper/2406.00019/) (2024)
- [MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training](https://mlsanity.com/paper/2510.12831/) (2025)
- [SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation](https://mlsanity.com/paper/2310.18376/) (2023)
- [MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation](https://mlsanity.com/paper/2502.11022/) (2025)
- [Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2306.04480/) (2023)
- [T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing](https://mlsanity.com/paper/2306.08368/) (2023)
- [QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.06655/) (2023)
- [SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset](https://mlsanity.com/paper/2208.12711/) (2022)
- [Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead](https://mlsanity.com/paper/2302.11054/) (2023)
- [LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios](https://mlsanity.com/paper/2410.11457/) (2024)
- [Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL](https://mlsanity.com/paper/2603.05996/) (2026)
- [Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model](https://mlsanity.com/paper/2605.22843/) (2026)

## Abstract (arXiv)

Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logic-form SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pre-trained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-the-art results on the two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.