One-shot Text-to-SQL via Candidate Search and Matching Networks
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Solution Overview
Problem
Existing natural language processing systems for translating human language into SQL queries require extensive training data and cannot accurately recognize unobserved templates, limiting their adaptability and precision.
Innovation Solution
A one-shot learning architecture combining deep learning neural networks, including a candidate search network and a matching network, to reduce the SQL template set and fill in slots with natural language inputs, enabling the generation of SQL queries from a single example without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning systems are used for text-to-SQL translation, then the system can learn from training data, but the model cannot accurately recognize speech inputs it has not been previously trained on
Solution Approach 1:
The patent segments the text-to-SQL translation process into distinct modules: a speech recognition system that identifies speech inputs, a template matching component that compares against a database of speech templates, and a SQL generation module. This segmentation allows the system to handle both trained and untrained speech patterns by matching against templates rather than relying solely on trained models.
Solution Approach 2:
The patent creates a comprehensive database of speech templates that copies and stores various speech patterns and their corresponding SQL translations. When a new speech input is encountered, the system searches for matching templates in this database, allowing it to handle unobserved speech patterns by finding similar template matches rather than requiring retraining.
2Reliability
If extensive training data is used to improve model robustness, then the model can recognize common cases better, but the system requires large amounts of training data and cannot adapt to new templates
Solution Approach 1:
The patent performs preliminary action by pre-collecting and storing a comprehensive database of speech templates and their corresponding SQL translations before deployment. This pre-prepared template database eliminates the need for extensive training data during operation, as the system can directly match speech inputs against the pre-existing templates.
Solution Approach 2:
The patent creates a universal speech template database that serves multiple functions: it stores trained speech patterns for accurate recognition, provides matching templates for unobserved speech patterns, and enables adaptation to new templates without retraining. This single template database replaces the need for separate training data sets for different speech patterns.
3Ease of manufacture
If rule-based approaches are used for natural language processing, then the system can parse and pattern match speech to particular words, but the approach is of low precision and only effective in specific use cases
Solution Approach 1:
The patent introduces speech templates as an intermediary layer between rule-based parsing and SQL generation. Instead of directly mapping speech to SQL using rigid rules, the system first matches speech against stored templates, which then guide the SQL generation process. This intermediary template matching layer improves precision while maintaining the simplicity of rule-based approaches.
Data Source
AI summary
Provided is a system and method for detecting a SQL command from a natural language input using neural networks which works even when the SQL command has not been seen before by the neural networks. In one example, the method may include storing a candidate set comprising structured query language (SQL) templates paired with respective text values, reducing, via a first predictive network, the candidate set into a subset of candidates based on a natural language input and the text values included in the candidate set, selecting, via a second predictive network, an SQL template from among the subset of candidates based on the natural language input and text values included in the subset of candidates, and determining a SQL command that corresponds to the natural language input based on the selected SQL template and content from the natural language input.