Language Model Data Augmentation for Text Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Textual data augmentation for classification tasks is challenging due to the scarcity of labeled data, as existing methods often produce grammatically and semantically incorrect data, making it difficult to improve model performance effectively.
Innovation Solution
A method that fine-tunes a pre-trained language model using a small training dataset, synthesizes new labeled sentences, and filters them using a classifier to generate an augmented training dataset, ensuring the quality of the synthesized data, which is then used to improve text classification tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data augmentation methods (synonym replacement, word deletion, word order changing) are applied to textual data, then the quantity of training data increases, but the quality of synthesized data deteriorates due to grammatical and semantic incorrectness
Solution Approach 1:
The patent introduces a pre-trained language model as an intermediary between the augmentation process and the final synthesized data. This language model generates contextually appropriate sentences that maintain grammatical and semantic correctness, resolving the contradiction between increasing data quantity and maintaining data quality
Solution Approach 2:
The patent changes the fundamental parameter of data generation from simple word-level transformations to sentence-level generation using language models. This parameter change enables the synthesis of grammatically correct and semantically meaningful sentences, thereby maintaining data quality while increasing data quantity
2Manufacturing precision
If a pre-trained language model is fine-tuned and used for synthesizing labeled data, then the quality and diversity of augmented data improves, but the complexity of the system increases
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on large corpora before fine-tuning it for specific classification tasks. This preliminary pre-training establishes a strong foundation that simplifies the subsequent fine-tuning process and data synthesis, managing system complexity while maintaining high data quality
Solution Approach 2:
The patent segments the data augmentation process into distinct stages: pre-training phase, fine-tuning phase, and synthesis phase. This segmentation allows each component to be optimized independently, managing overall system complexity while achieving high-quality augmented data
3Manufacturing precision
If labeled data is obtained through manual annotation to ensure high quality, then the quality of training data improves, but the time and cost required increases significantly
Solution Approach 1:
The patent implements self-service by enabling the language model to automatically generate high-quality labeled training data without requiring manual annotation. The model synthesizes sentences with automatic label assignment, eliminating the time-consuming and costly manual labeling process while maintaining data quality through the model's inherent linguistic understanding
Data Source
AI summary
Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.