Machine Learning Training Data Simplification and Variant Generation
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Solution Overview
Problem
Machine learning models, particularly those for natural language processing, require a large volume of training data to account for various modifications and irregular usage, making it desirable to reduce the necessary training data while maintaining adequate model training.
Innovation Solution
A method that involves receiving a single instance of training data, simplifying it to create simplified data, generating multiple training data variants, and training a machine learning model using these variants, thereby reducing the amount of training data required while accounting for irregularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large volume of training data is used to account for various modifications and irregular usage in natural language, then the model training accuracy and reliability are improved, but the data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent generates multiple synthetic copies (variants) of a single simplified training data instance by applying different transformations and modifications. These copies are then used to train the machine learning model, allowing the model to learn from diverse examples without requiring a large volume of unique training data. This directly addresses the contradiction by reducing data volume while maintaining training reliability through synthetic data replication.
Solution Approach 2:
The patent transforms the training data by changing various parameters such as syntax, wording, and structure while maintaining the core meaning. The single simplified training instance is modified into multiple variants with different parameters (e.g., different grammatical forms, synonyms, rephrased sentences). This allows the model to learn robust patterns from fewer data points by exposing it to multiple parameter variations of the same underlying concept.
2Quantity of substance
If a single instance of training data is simplified and used to generate multiple variants, then the training data volume is reduced, but the complexity of data processing and model training increases
Solution Approach 1:
The patent performs preliminary simplification of the training data before generating variants. The original training data is first simplified to a core representation, and then multiple variants are generated from this simplified form. This preliminary action reduces the complexity of subsequent processing by working with a standardized simplified structure rather than raw complex data, making the overall process more manageable despite the additional variant generation step.
Solution Approach 2:
The patent segments the data processing task into distinct stages: (1) simplifying the original training data, (2) generating multiple variants from the simplified form, and (3) training the model on these variants. This segmentation allows each stage to be optimized independently and reduces overall complexity by breaking down the complex data transformation process into manageable steps.
3Productivity
If multiple training data variants are generated from simplified data, then the model can learn complex patterns with less data, but the time required for data preparation and model training increases
Solution Approach 1:
The system performs self-service by automatically generating variants from simplified training data without requiring manual intervention. The variant generation process is automated through algorithmic transformations applied to the simplified data structure. This eliminates the need for manual data creation and preparation, significantly reducing the time investment required while maintaining the ability to learn complex patterns from the generated variants.
Data Source
AI summary
A computer-implemented method according to one embodiment includes receiving a single instance of training data, simplifying the single instance of training data to create a single instance of simplified training data, generating a plurality of training data variants, based on the single instance of simplified training data, and training a machine learning model, utilizing the plurality of training data variants.