Conversational Table Querying With Pretrained NL-to-SQL Parsing
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Solution Overview
Problem
Existing technologies struggle to enable users to interact with tabular knowledge through human-computer dialogue effectively, particularly in structured query languages (SQL) databases, lacking efficient methods to convert natural language queries into SQL statements.
Innovation Solution
A human-computer dialogue method and apparatus that utilizes a pre-trained table model to parse natural language queries into SQL statements, incorporating a data synthesizer for automatic training data generation and a core engine with language understanding, management, and generation modules to facilitate interaction with tabular knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language queries are converted into SQL statements using existing technologies, then the conversion accuracy is insufficient, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary component (the pre-trained table model) that bridges natural language and SQL statements. This model acts as a mediator that has been specifically trained on tabular data structures, enabling accurate conversion without requiring complex rule-based systems or extensive manual configuration, thus improving conversion accuracy while controlling system complexity
Solution Approach 2:
The patent applies preliminary action by pre-training the table model on tabular data before actual use. This pre-training phase prepares the model to understand table structures, column relationships, and data patterns in advance, so that during actual query conversion, the model can directly apply this knowledge to achieve high accuracy without requiring complex runtime processing
2Ease of operation
If tabular knowledge is made accessible through human-computer dialogue, then the ease of operation improves, but the reliability of data retrieval decreases
Solution Approach 1:
The pre-trained table model serves as a reliable intermediary that ensures accurate mapping between natural language intent and SQL queries. By specializing in tabular data understanding, it maintains data retrieval reliability even while allowing users to interact through simple natural language rather than requiring SQL expertise
Solution Approach 2:
The system incorporates feedback mechanisms where the model learns from the interaction between user queries and table structures. This feedback loop allows the system to refine its understanding of user intent and table relationships, maintaining reliability while improving ease of operation through iterative learning from actual usage patterns
Data Source
AI summary
A human-machine conversation method and apparatus, a device, and a storage medium. The method comprises: acquiring a user statement of a current round; determining, from one or more candidate tables, one or more target tables that match the user statement; parsing the user statement of the current round to obtain a first query statement; according to the first query statement, querying from the one or more target tables to obtain target data; and generating a system reply of the current round according to the target data.