Sequence-Tagging Training with Weighted Pseudo-Labels
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Solution Overview
Problem
Existing sequence-tagging models often produce unsatisfactory results due to insufficient training examples, and manually producing relevant training sets is time and resource-intensive, making it difficult to remedy data scarcity issues.
Innovation Solution
A training framework using a teacher model to generate pseudo-labeled examples, which are then weighted based on validation information derived from labeled examples, including uncertainty and similarity factors, to improve the quality of the student model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual production of labeled training examples is used, then training data quality is improved, but time consumption and resource consumption increase significantly
Solution Approach 1:
The patent uses a teacher model to generate pseudo-labeled training examples by copying and adapting its predictions to create synthetic labeled data. This allows the system to produce large quantities of training data without manual annotation, significantly reducing time consumption while maintaining acceptable quality through subsequent filtering and weighting mechanisms
Solution Approach 2:
The system implements self-service by having the teacher model automatically generate pseudo-labeled training examples without human intervention. The model serves itself by producing the training data it needs, eliminating the dependency on manual annotation processes and reducing resource consumption
2Productivity
If pseudo-labeled training examples are used to reduce manual work, then resource consumption is reduced, but noise in training data increases
Solution Approach 1:
The patent applies local quality by differentiating between different pseudo-labeled training examples through confidence scoring and filtering. High-confidence pseudo-labeled examples are retained while low-confidence ones are discarded or downweighted, ensuring that only reliable pseudo-labeled data contributes to training, thus maintaining training data reliability while still benefiting from reduced resource consumption
Solution Approach 2:
The system implements feedback mechanisms where the student model's performance on validation data is continuously monitored. This feedback is used to adjust the weighting and selection of pseudo-labeled examples in subsequent training iterations, progressively improving training data reliability while maintaining resource efficiency
3Manufacturing precision
If a large number of labeled training examples are collected, then model training quality is improved, but data collection complexity and cost increase
Solution Approach 1:
The patent uses the teacher model to copy and generate synthetic labeled training data, creating large volumes of training examples without the complexity of manual data collection. This approach maintains model training quality by producing diverse, domain-relevant pseudo-labeled examples while dramatically reducing data collection complexity
Solution Approach 2:
The system performs preliminary action by pre-processing unlabeled data through the teacher model to create pseudo-labeled training examples before actual training begins. This preliminary labeling action prepares the training data in advance, eliminating the need for complex real-time data collection and annotation processes
Data Source
AI summary
A technique iteratively updates model weights of a teacher model and a student model. In operation, the teacher model produces noisy original pseudo-labeled training examples from unlabeled training examples. The technique weights the original pseudo-labeled training examples based on validation information. The technique then updates model weights of the student model based on the weighted pseudo-labeled training examples. The validation information, which is used to weight the original pseudo-labeled training examples, is produced by selecting labeled training examples based on an uncertainty-based factor and a similarity-based factor. The uncertainty-based factor describes an extent to which the student model produces uncertain classification results for the set of labeled training examples. The similarity-based factor describes the similarity between the set of labeled training examples and the unlabeled training examples. Overall, the technique is efficient because it eliminates the need to produce a large number labeled training examples.