Document Recognizer Training Feedback for Limited Labeled Data
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Solution Overview
Problem
Conventional entity extraction tools lack sufficient feedback on how to maximize the use of limited labeled ground truth data, particularly for documents with complex layouts or lower-quality annotated examples, leading to suboptimal training of document recognition models.
Innovation Solution
A document evaluator that randomly selects subsets of annotated documents to train multiple models, providing users with evaluations of model quality and variability, allowing for informed curation of training samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a small quantity of annotated documents is used for training, then training time and computational resources are reduced, but model quality and accuracy deteriorate
Solution Approach 1:
The system implements feedback by evaluating multiple models trained on different random subsets of annotated documents and providing quality assessments to users. This feedback mechanism allows users to understand the impact of limited training data and make informed decisions about data curation, thereby improving model quality even with constrained training resources.
Solution Approach 2:
The system trains multiple models on partial subsets of the available annotated documents rather than training a single model on all data. By randomly selecting different subsets and training multiple models, the system explores the impact of data quantity and quality, enabling users to identify the minimum effective training set size for acceptable model performance.
2Manufacturing precision
If a larger quantity of annotated documents is used for training, then model quality improves, but training time and computational resources increase
Solution Approach 1:
The system segments the annotated documents into multiple random subsets and trains separate models on each subset. This segmentation approach allows parallel training of multiple models with smaller data portions, reducing the computational burden on individual training runs while still evaluating the impact of data quantity on model quality through aggregation of multiple results.
Solution Approach 2:
The system performs preliminary evaluations by training multiple models on random subsets before committing to full-scale training. This preliminary action provides users with quality assessments and variability information that guide decisions on whether to proceed with full training using all annotated documents or to curate a smaller, more effective training set.
3Loss of information
If multiple models are trained on different subsets, then feedback on data quality and variability is improved, but computational resources and training time increase
Solution Approach 1:
The system trains a limited number of models on random subsets rather than exhaustively training all possible model configurations. This partial action provides sufficient feedback on data quality and variability to guide user decisions without requiring excessive computational resources, balancing information quality with resource constraints.
4Reliability
If random subsets of annotated documents are selected for training, then evaluation of model variability is improved, but the complexity of the training process increases
Solution Approach 1:
The system automatically performs random subset selection, model training, and quality evaluation without requiring manual intervention. This self-service approach simplifies the user experience despite the underlying process complexity, as users simply provide annotated documents and receive comprehensive quality assessments and variability information.
Data Source
AI summary
A method includes receiving, from a user device associated with a user, a plurality of annotated documents. Each respective annotated document includes one or more fields and each respective field labeled by a respective annotation. The method includes, for a threshold number of iterations, randomly selecting a respective subset of annotated documents from the plurality of annotated documents; training a respective model on the respective subset of annotated documents; and generating, using the plurality of annotated documents not selected for the respective subset of annotated documents, a respective evaluation of the respective model. The method also includes providing, to the user device, each respective evaluation.