NLP Training Set Augmentation via Textual Unit Importance
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Solution Overview
Problem
Natural language processing model training sets require extensive human-labeled samples, making expansion costly and time-consuming, while random augmentation can reduce accuracy by altering relevant characteristics, leading to poor generalization on new inputs.
Innovation Solution
A system that programmatically augments training sets by identifying and generating variants of textual units based on their importance and task-specific rules, using algorithms like LIME and tf-idf to ensure relevance and maintain data quality, thereby enhancing model accuracy and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training sets are expanded using human-labeled samples, then model accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system creates synthetic copies of training samples by generating variants through textual unit substitutions. Instead of manually creating new labeled samples, the system automatically generates synthetic training data by replacing textual units with synonyms or alternative expressions, thereby expanding the training set without proportional increases in human labeling time.
Solution Approach 2:
The system performs self-augmentation by automatically generating and validating training samples without requiring external human intervention for each new sample. The importance determination module and variant generation work together in an automated pipeline that expands the training set independently, reducing reliance on continuous human input.
2Quantity of substance
If training sets are expanded using random augmentation, then quantity of samples increases, but model generalization deteriorates
Solution Approach 1:
The system applies different treatment to different parts of the training data based on their importance. The importance determination module identifies which textual units are critical for model performance and applies variant generation selectively. High-importance textual units are preserved or subtly modified, while lower-importance units are more freely augmented, maintaining local data quality where it matters most.
Solution Approach 2:
The system transforms training samples by changing specific parameters of the text—such as replacing individual textual units with synonyms or alternative expressions—while preserving the overall semantic meaning and structure. This controlled parameter change allows sample expansion without compromising the fundamental characteristics that enable good generalization.
3Adaptability or versatility
If textual units are altered during augmentation, then sample diversity increases, but task-relevant characteristics are degraded
Solution Approach 1:
The system uses the importance determination module to provide feedback on which textual units can be safely modified. By evaluating the impact of potential substitutions on task performance, the system receives feedback that guides the augmentation process, ensuring that modifications to textual units do not degrade the characteristics essential for the NLP task.
Solution Approach 2:
The system applies partial augmentation by selectively modifying only certain textual units rather than altering entire samples. By focusing substitutions on less critical textual units and preserving key task-relevant features, the system achieves sufficient diversity without excessive modification that would harm performance.
Data Source
AI summary
A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.