Cross-Domain Text-to-SQL Parsing with Anchor Text Alignment
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Solution Overview
Problem
Conventional text-to-SQL semantic parsing systems are resource-intensive and unscalable due to the need for separate parsers for each database, as they struggle to handle different database schemas and cross-database queries effectively.
Innovation Solution
A sequential text-database encoding framework that contextualizes question and schema representations using large-scale pre-trained language models like BERT, with anchor texts and pointer-generator decoders to align natural language queries with database structures, enabling cross-database query translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic parsers are trained for each individual database, then parsing accuracy for that database is improved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The patent applies universality by developing a single semantic parser that can handle multiple databases with different schemas. The parser uses a pre-trained transformer language encoder and anchor text mechanism to generalize across domains, eliminating the need for separate parsers for each database while maintaining parsing accuracy through cross-domain adaptation
2Adaptability or versatility
If conventional semantic parsers are trained on large-scale datasets from numerous databases, then cross-database capability is improved, but the parser cannot reliably handle different schemas due to memorization limitations
Solution Approach 1:
The patent introduces anchor text as an intermediary mechanism that bridges the gap between natural language queries and database schemas. The anchor text represents database entities and serves as a mediator that allows the parser to generalize query patterns across different schemas without relying on memorization, thereby improving both cross-database capability and query translation accuracy
3Reliability
If separate semantic parsers are developed for each database, then parsing performance is optimized for that database, but the process becomes unscalable and resource intensive
Solution Approach 1:
The patent implements a universal semantic parser that can serve multiple databases simultaneously. By using a pre-trained transformer encoder and anchor text mechanism, the system achieves reliable parsing performance across diverse schemas without requiring separate parsers for each database, thereby improving scalability and reducing resource consumption
Data Source
AI summary
A text-to-database neural network architecture is provided. The architecture receives a natural language question and a database schema and generates a serialized question-schema representation that includes a question and at least one table and at least one field from the database schema. The serialized question-schema representation is appended with at least one value that matches a word in the natural language question and at least one field in a database picklist. An encoder in the architecture generates question and schema encodings from the appended question-schema representation. Schema encodings are associated with metadata that indicates a data type of the fields and whether fields are associated with primary or foreign keys. A decoder in the architecture generates an executable query from the question encodings and schema encodings.