Pseudo Labelling Pipeline for Scalable ML Training Data
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Solution Overview
Problem
Current methods for creating training datasets for machine learning algorithms, especially for text data classification, are time-consuming and costly, often requiring manual annotation and are not efficient for large-scale, high-quality dataset generation, particularly in specialized domains like customer service interactions.
Innovation Solution
A pipeline that selects a subset of data from an initially labelled dataset, refines labels using annotation and historical information, and extracts additional data based on similarities to create a large, high-quality training dataset, reducing the need for extensive human annotation and enabling efficient generation of large-scale datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to create training datasets, then label accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables datasets to annotate themselves by leveraging the training model's predictions on unlabeled data. The model generates pseudo-labels for new data, which are then used to expand the training dataset without requiring manual annotation, thus achieving self-service data labeling.
Solution Approach 2:
The system creates copies of the training process by using the trained model to generate pseudo-labels for unlabeled data. These pseudo-labeled data serve as copies of the annotation process, allowing rapid expansion of the dataset without repeating manual annotation work.
2Productivity
If template-based or keyword-based inductive methods are used, then dataset creation speed is improved, but accuracy and suitability for specialized domains deteriorate
Solution Approach 1:
The system implements a feedback loop where the training model's predictions are continuously refined. Pseudo-labeled data are added to the training set, the model is retrained, and this process iterates to improve accuracy while maintaining high productivity through automated expansion.
Solution Approach 2:
The system dynamically adjusts the parameters of the training process by iteratively adding pseudo-labeled data and retraining the model. This changes the training dataset composition and model parameters over time, improving accuracy while maintaining automated high-speed dataset creation.
3Reliability
If extensive manual annotation is performed to achieve high-quality datasets, then data quality is improved, but the scalability and efficiency deteriorate
Solution Approach 1:
The system achieves scalable data quality improvement by enabling the training process to automatically generate and validate its own training data. The model serves itself by generating pseudo-labels that maintain quality standards while allowing unlimited dataset expansion without proportional increases in manual annotation effort.
Solution Approach 2:
The system performs preliminary annotation through the trained model before manual review. By pre-generating pseudo-labels with high confidence scores, the system prepares data that requires minimal manual verification, thus improving scalability while maintaining quality through selective manual validation of uncertain cases.
Data Source
AI summary
Techniques for generating training datasets for machine learning algorithms are disclosed. An initial labelled dataset may be a noisy dataset with multiple misclassifications in labelling of the data. Human-based annotation and the application of historical data information are implemented to refine labels for a subset of data from the initial labelled dataset. After refinement of the subset of data, data with existing labels is extracted from the initial labelled dataset to add to the refined subset and generate a training dataset. The data that is extracted from the initial labelled dataset is data that is similar to data in the refined subset with the same label as the extracted data. The extraction of data according to similarities in the data is applied to scale the subset of data to a larger dataset while maintaining quality in order to provide a large, high-quality training dataset for the machine learning algorithm.