Natural Language Interfaces with Interpretable SQL for Cross-Database Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language database interfaces (NLDB) face challenges in achieving high accuracy for cross-database queries, leading to mistrust from users due to high error rates and opacity, and lack the ability to easily transfer to new databases without a domain-specific corpus.
Innovation Solution
A system with a semantic parser that generates interpretable SQL queries, uses a hybrid of synchronous context-free grammars for explanations, and includes a safe-guard module to detect out-of-domain and hard-to-answer questions, allowing users to verify the correctness of the queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language to SQL translation is performed without domain-specific corpus, then cross-database transferability is improved, but translation accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of the target database schema and generates database-specific guidance information before executing the natural language to SQL translation. This preliminary action enables the system to adapt to new databases without requiring domain-specific training corpora, while maintaining high translation accuracy through pre-computed schema insights.
Solution Approach 2:
The patent introduces an intermediary component that generates database-specific guidance information acting as a bridge between the natural language query and the target database schema. This intermediary translates general natural language intent into database-specific SQL queries by leveraging pre-analyzed schema characteristics, thereby enabling accurate cross-database transfer without domain-specific corpora.
2Ease of operation
If traditional NLDB systems are used, then simplicity of interface is improved, but user trust deteriorates due to high error rates and opacity
Solution Approach 1:
The system implements a feedback mechanism that provides users with explanations of the generated SQL queries and allows them to request modifications. This feedback loop increases user trust by making the system's reasoning transparent while maintaining interface simplicity, as users can verify the correctness of queries before execution without needing to understand complex database operations.
Solution Approach 2:
The patent enables users to self-correct or modify generated SQL queries through an intuitive interface. Users can request changes to the generated queries and the system will regenerate appropriate SQL statements, allowing users to take control when needed while maintaining the simplicity of natural language interaction for routine queries.
Data Source
AI summary
A computer system and method for answering a natural language question is provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises receiving a natural language question, generating a SQL query based on the natural language question, generating an explanation regarding a solution to the natural language question as answered by the SQL query, and presenting the solution and the explanation.