Logical-Form Dialogue Generator for Multi-Turn NL2QL Data Synthesis
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Solution Overview
Problem
Conventional data collection methods for query-response systems are limited by their domain dependence, reduced accuracy, and rigid model architecture, leading to decreased applicability and performance across different software domains.
Innovation Solution
A logical-form dialogue generator is employed to construct natural-language-to-query-language (NL2QL) datasets, utilizing a logical-form specification to generate diverse and coherent conversational datasets, which are domain-independent and reduce user input errors, enabling more flexible and accurate training data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional (semi)automatic data-generation systems are used to generate training datasets, then the data generation process can be automated, but the generated datasets are highly tailored for training a specific machine-learning model based on a particular knowledge base or software domain, undermining cross-application of query-response systems
Solution Approach 1:
The patent applies universality by creating a data generation system that produces domain-independent training datasets. The system generates synthetic question-answer pairs that can be applied across multiple domains and knowledge bases, not just tailored to a single domain. This is achieved through parameterized templates and controlled generation processes that ensure broad applicability while maintaining automation.
2Quantity of substance
If large amounts of user input processing is performed for labelling and annotation in conventional data collection methods, then more training data can be generated, but the training data becomes error-prone, subjective, and inconsistent
Solution Approach 1:
The patent applies self-service by using automated synthetic data generation that does not require manual user input for labelling or annotation. The system generates training data autonomously through controlled processes, eliminating human errors, subjectivity, and inconsistencies associated with manual processing while maintaining high data quality and consistency.
3Device complexity
If conventional data generation systems only analyze and generate first-turn utterances or accept predetermined dialogue sequences, then the system architecture remains simple, but the system lacks flexibility and requires multiple user feedback loops and re-training steps
Solution Approach 1:
The patent applies dynamics by creating a flexible data generation system that can handle both single-turn and multi-turn dialogue sequences. The system dynamically adapts its generation process based on the required dialogue complexity, allowing it to generate coherent multi-turn conversations while maintaining a relatively simple underlying architecture through parameterized control rather than complex structural changes.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.