# An End-to-end Neural Natural Language Interface for Databases

arXiv: https://arxiv.org/abs/1804.00401 · HTML: https://mlsanity.com/paper/1804.00401/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer](https://mlsanity.com/paper/2110.03546/) (2021)
- [UNITE: A Unified Benchmark for Text-to-SQL Evaluation](https://mlsanity.com/paper/2305.16265/) (2023)
- [SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps](https://mlsanity.com/paper/2510.27532/) (2025)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [Retrieval and Augmentation of Domain Knowledge for Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2510.02394/) (2025)

## Abstract (arXiv)

The ability to extract insights from new data sets is critical for decision making. Visual interactive tools play an important role in data exploration since they provide non-technical users with an effective way to visually compose queries and comprehend the results. Natural language has recently gained traction as an alternative query interface to databases with the potential to enable non-expert users to formulate complex questions and information needs efficiently and effectively. However, understanding natural language questions and translating them accurately to SQL is a challenging task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet made their way into practical tools and commercial products. In this paper, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests partial query extensions to users during query formulation and thus helps to write complex queries.