Transformer Field Extraction with Progressive Pseudo-Label Refinement
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Solution Overview
Problem
Existing methods for field extraction from form documents require significant human effort and field-level annotations, which are costly and labor-intensive due to the sensitivity and limited availability of labeled data, making them inefficient and impractical.
Innovation Solution
A self-supervised field extraction system that uses pseudo-labels from unlabeled forms, leveraging a transformer-based structure and a refinement module with progressive label ensembles (PLEs) to refine pseudo-labels, reducing the need for field-level annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional field extraction methods are used with human workers and field-level annotations, then extraction accuracy can be maintained, but the process becomes labor-intensive and costly
Solution Approach 1:
The system performs self-service by automatically generating pseudo-labels from unlabeled form documents using rule-based methods. The field extraction model trains itself on these self-generated labels, eliminating the need for manual annotation while achieving accurate field extraction. This self-service mechanism resolves the contradiction by maintaining extraction accuracy through automated self-training rather than human annotation.
Solution Approach 2:
The system performs preliminary action by generating pseudo-labels before actual model training. Rule-based methods create initial labels from unlabeled data, which then serve as training data for the field extraction model. This preliminary labeling action enables subsequent automated training without requiring human workers, thus improving productivity while maintaining accuracy through the preliminary preparation of training data.
2Reliability
If field-level annotations are used for training, then model performance improves, but the cost and time required for data preparation increases significantly
Solution Approach 1:
The system eliminates time-consuming manual annotation by implementing self-service through automated pseudo-label generation. The field extraction model generates its own training labels from unlabeled form documents using rule-based methods, then trains on these self-generated labels. This approach maintains model performance while eliminating the time loss associated with human annotation.
Solution Approach 2:
The system extracts the time-consuming annotation process from the training pipeline by replacing it with automated pseudo-label generation. The rule-based methods extract meaningful labels directly from unlabeled form documents, removing the need for human annotators and significantly reducing data preparation time while maintaining model performance through automated self-training.
3Adaptability or versatility
If unlabeled data is used without pseudo-labels, then data availability increases, but extraction accuracy decreases without proper labeling
Solution Approach 1:
The system introduces pseudo-labels as an intermediary between unlabeled data and the field extraction model. Rule-based methods generate these intermediate labels from unlabeled form documents, serving as a bridge that enables the model to learn from previously unusable unlabeled data. This intermediary mechanism maintains extraction accuracy by providing structured training signals while maximizing data availability through the use of unlabeled documents.
Solution Approach 2:
The system changes the labeling parameter from requiring manual field-level annotations to using automated pseudo-labels generated by rule-based methods. This parameter change in the labeling approach enables the system to utilize unlabeled data effectively, maintaining extraction accuracy by transforming unlabeled data into labeled training data through automated rule-based labeling.
4Productivity
If more human workers are deployed for field extraction, then processing volume increases, but labor costs and operational complexity increase
Solution Approach 1:
The system replaces the mechanical system of human workers with an automated field extraction model. The model processes form documents automatically using learned patterns from pseudo-labeled training data, increasing processing volume without adding human labor. This substitution eliminates operational complexity associated with managing human workers while maintaining high productivity through automated processing.
Data Source
AI summary
Embodiments described a field extraction system that does not require field-level annotations for training. Specifically, the training process is bootstrapped by mining pseudo-labels from unlabeled forms using simple rules. Then, a transformer-based structure is used to model interactions between text tokens in the input form and predict a field tag for each token accordingly. The pseudo-labels are used to supervise the transformer training. As the pseudo-labels are noisy, a refinement module that contains a sequence of branches is used to refine the pseudo-labels. Each of the refinement branches conducts field tagging and generates refined labels. At each stage, a branch is optimized by the labels ensembled from all previous branches to reduce label noise.