Abstract
The efficient interaction with databases for non-technical users remains a significant challenge. Natural language interfaces to databases (NLIDBs) offer a promising solution by translating user queries in natural language to Structured Query Language (SQL). However, existing deep learning-based NL2SQL models heavily rely on large, manually-curated training datasets, which is a costly and time-consuming process that hinders the adoption of NLIDBs for new database schemas. This paper presents DBPal, a novel and fully pluggable training pipeline that addresses this bottleneck. DBPal leverages weak supervision to automatically synthesize high-quality, diverse training data directly from a given database schema, thereby eliminating the need for manual data annotation. Our methodology includes several data augmentation techniques to enhance the model's robustness to linguistic variations and to better generalize to unseen queries. By providing a scalable and automated method for training data generation, DBPal significantly improves the translation accuracy of state-of-the-art NL2SQL models, making NLIDBs more accessible and practical for a wide range of applications.

1. Introduction
   In today's data-driven world, the ability to efficiently query and analyze information stored in relational databases is paramount. However, this task typically requires a working knowledge of SQL and an understanding of the underlying database schema. While this is manageable for data professionals, it presents a significant barrier for non-technical users who could benefit from direct access to data. Natural Language Interfaces to Databases (NLIDBs) aim to bridge this gap by allowing users to interact with databases using everyday language.

Recent advances in deep learning, particularly in neural machine translation (NMT), have led to significant progress in NL2SQL systems. These models treat the translation from natural language to SQL as a sequence-to-sequence problem. While powerful, their performance is highly dependent on the quality and quantity of the training data. The need to manually create large, paired datasets of natural language queries and their corresponding SQL statements is a major limitation, often making the development of a new NLIDB for a different database schema impractical.

DBPal is designed to overcome this fundamental limitation. By implementing a novel training pipeline, DBPal automatically generates synthetic training data with minimal input (only the database schema is required). This approach allows any existing NL2SQL model to be bootstrapped for a new database without the need for manual data curation.

2. Methodology
   The DBPal training pipeline operates on the principle of weak supervision to synthesize training data. This process is fully automated and consists of several key stages.

2.1 Training Data Synthesis
The core of DBPal is its ability to generate natural language-SQL pairs. The pipeline uses the database schema (table names, column names, data types, and relationships) to create a vast number of SQL query templates. These templates are then populated with actual data values from the database. A natural language query corresponding to each SQL template is then automatically generated using a set of predefined rules and grammar-based techniques. This synthetic data generation process is highly scalable, producing a comprehensive dataset that covers a wide range of query types.

2.2 Data Augmentation for Robustness
To improve the model's robustness to linguistic variations, DBPal incorporates several data augmentation techniques. These techniques are crucial for ensuring that the model can handle the diversity of ways users might phrase a query. For example, a paraphrasing process, which utilizes an off-the-shelf paraphrasing database, is used to generate multiple semantically equivalent but lexically different natural language queries for a single SQL statement. This exposes the model to a greater variety of input styles and makes it less susceptible to slight changes in wording.

2.3 Pluggable Architecture
A key design principle of DBPal is its "pluggable" nature. The pipeline is model-agnostic and can be used to generate training data for any existing NL2SQL deep learning model (e.g., SyntaxSQLNet, Seq2SQL). The output of the pipeline is a dataset that can be directly fed into the training routine of the chosen model. This modularity allows developers to leverage the latest advancements in deep learning without having to re-engineer their entire data preparation process.

3. System Architecture
   The DBPal system is built on a modular architecture that separates the data generation pipeline from the end-user interface. The system comprises three main components: a web-based front-end, the training pipeline, and the neural query translator.

User Interface: The web interface provides a simple text box for users to enter their natural language queries. It also features a learned auto-completion model that provides real-time suggestions to guide users toward more precise and translatable queries.

Training Pipeline: This is the core of DBPal, responsible for the automated synthesis and augmentation of training data.

Neural Query Translator: This is a deep learning model trained on the data generated by the DBPal pipeline. At runtime, it translates incoming natural language queries from the user into SQL statements.

4. Conclusion
   DBPal offers a novel and highly effective solution to a critical problem in the development of Natural Language Interfaces to Databases. By automating the process of training data generation through weak supervision and data augmentation, DBPal eliminates the need for expensive and time-consuming manual annotation. Our approach significantly improves the performance of existing NL2SQL models, making it feasible to create robust and accurate NLIDBs for new database schemas. Future work will focus on extending the data synthesis techniques to handle more complex query types, such as multi-table joins and nested subqueries, and exploring the integration of conversational context to support multi-turn interactions.

5. Output