Language Model Data Augmentation for Syntactic and Semantic Context
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Solution Overview
Problem
Current machine learning models require large amounts of labeled data for accurate training, which is time-consuming and prone to human bias, and existing automatic labeling methods fail to account for syntactic and semantic nuances.
Innovation Solution
Utilize an ML language model to predict tokens similar to a seed example, recursively tracking syntactic and semantic relationships, and combine these tokens to form candidate examples, which are then selected and combined with the original dataset using pseudo-reinforcement learning to create an augmented training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training data is used, then labeling accuracy and quality are improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system uses machine translation models to generate synthetic training data that copies the linguistic patterns and structures from high-quality source data, creating augmented training sets without requiring manual relabeling of each synthetic example
Solution Approach 2:
The machine translation model performs self-training by using its own generated translations to create training data, eliminating the need for human annotators to review and validate each synthetic example individually
2Loss of time
If automatic labeling methods are used, then time consumption is reduced, but syntactic and semantic nuances are lost
Solution Approach 1:
The system changes the parameters of the machine translation model by fine-tuning it on domain-specific data and adjusting translation strategies to preserve syntactic structures and semantic meanings while maintaining automated processing speeds
Solution Approach 2:
The system implements feedback mechanisms where the machine translation model continuously refines its output based on evaluation metrics that detect syntactic and semantic quality, allowing iterative improvement of translation accuracy
3Measurement precision
If more labeled training data is used, then model parameter tuning accuracy is improved, but data collection cost and complexity increase
Solution Approach 1:
The system creates synthetic training data by copying and transforming existing labeled data through machine translation, generating multiple variations of training examples that expand the dataset without requiring new manual annotations
Solution Approach 2:
The system generates excessive synthetic data beyond what is immediately needed, creating a large augmented training set that provides robustness and redundancy, allowing the model to be trained on more data than would traditionally be collected manually
Data Source
AI summary
Disclosed are embodiments for improving training data for machine learning (ML) models. In an embodiment, a method is disclosed where an augmentation engine receives a seed example, the seed example stored in a seed training data set; generates an encoded seed example of the seed example using an encoder; inputs the encoded seed example into a machine learning model and receives a candidate example generated by the machine learning model; determines that the candidate example is similar to the encoded seed example; and augments the seed training data set with the candidate example.