Synthetic Training Data Generation for Semantic Parsing
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Solution Overview
Problem
The high demand for large amounts of labeled training data for machine learning (ML) models in semantic parsing systems is hindered by the time-consuming and costly process of data collection and annotation, which also raises privacy and permission issues.
Innovation Solution
Generating ML training data instances by sampling formula grammar and natural language (NL) templates to create a formula tree, resulting in synthetic training data that can efficiently produce high-quality data across various NL usage scenarios, thus alleviating the need for human-collected data and addressing privacy concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-collected and labeled training data is used, then the quality and accuracy of semantic parsing models is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The patent uses template-based generation to create synthetic training data instances that copy the structural patterns of real NL-to-formula mappings. By using formula grammars and NL templates as blueprints, the system generates realistic training examples without manual annotation, resolving the contradiction between data quality and annotation time.
Solution Approach 2:
The system performs self-service by automatically generating training data using domain specifications, formula grammars, and NL templates. The generation process is autonomous, requiring no human annotators, thus eliminating the time-consuming manual labeling process while maintaining data quality through structured template-based synthesis.
2Adaptability or versatility
If human-collected training data is used, then the model learns from real-world NL variations, but privacy and permission issues arise
Solution Approach 1:
Instead of using real human data that raises privacy concerns, the patent copies the essential structural patterns of NL-to-formula mappings through templates. This synthetic approach maintains adaptability to NL variations while eliminating privacy risks by never handling actual user data.
Solution Approach 2:
The patent introduces templates and formula grammars as intermediaries between real-world NL variations and the training data. These intermediaries capture the essential patterns and variations of natural language while anonymizing the data, thus maintaining model adaptability without exposing privacy-sensitive information.
3Measurement precision
If more training data is generated to improve model performance, then the model accuracy increases, but the resource consumption and cost increase
Solution Approach 1:
The template-based generation system efficiently produces large volumes of training data by reusing formula grammars and NL templates. This copying approach allows rapid generation of diverse training instances without proportional increases in resource consumption, as the same templates can generate multiple variations through parameter substitution.
Solution Approach 2:
The formula grammars and NL templates serve as universal blueprints that can generate multiple training instances across different domains and scenarios. This multi-functionality allows a single set of templates to produce diverse training data, improving data volume and model accuracy without proportionally increasing development resources.
Data Source
AI summary
Examples described herein generate training data for machine learning (ML) for natural language (NL) processing (such as semantic parsing for translating NL). A formula tree is generated based on sampling both a formula grammar and NL templates. Using the formula tree, an ML training data instance pair is generated comprising a formula example and an NL example. A context example may also be used during instantiation of the formula tree. An ML model is trained with training data including the ML training data instance pair, and ML output is generated from NL input. The ML output includes, for example, a machine-interpretable formula, a database querying language command, or a general programming language instruction. Some examples support context-free grammar, probabilistic context-free grammar, and/or non-context-free production rules.