Document Language Model Pretraining for Low-Data Classification
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Solution Overview
Problem
Document classification and segmentation are challenging due to visual variability within document types, making it difficult to obtain sufficient training data and segment multiple documents effectively.
Innovation Solution
A deep learning-based system that utilizes a document language model with transformer encoder layers and a self-attention fusion module, pre-trained using self-supervised and contrastive learning strategies, to generate semantic representations of documents, which are then classified using a distance-based classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning is used for document classification, then the model can achieve accurate classification, but it requires large amounts of labeled training data which is difficult to obtain due to visual variability in document types
Solution Approach 1:
The system performs self-supervised pretraining on unlabeled documents before fine-tuning with labeled data. This preliminary action allows the model to learn general document representations and structures without requiring labeled annotations, thereby reducing the quantity of labeled training data needed while maintaining classification accuracy
Solution Approach 2:
The system uses self-supervised learning where the model learns from unlabeled documents by predicting document structures, layouts, and relationships without human annotation. This self-service approach generates its own training signals from raw documents, eliminating the need for expensive manual labeling while building robust document understanding capabilities
2Adaptability or versatility
If the system processes multiple document types with high visual variability, then it can handle diverse document categories, but it becomes difficult to segment and classify documents effectively without sufficient training examples for each type
Solution Approach 1:
The system segments documents into structural components (headers, bodies, footers, tables, images) and processes each component separately using specialized sub-models. This segmentation allows the system to handle diverse document types reliably by focusing on local structural patterns rather than requiring global document type knowledge, thereby improving segmentation accuracy across varied document categories
Solution Approach 2:
The system employs a universal document representation framework that can process multiple document types through a single trained model. The pretraining phase teaches the model general document structures and relationships that apply across all document types, enabling the system to maintain reliable segmentation and classification performance across diverse categories without needing separate models for each document type
3Measurement precision
If more training data is collected to improve classification accuracy, then the model performance increases, but the computational resources and time required for training increase significantly
Solution Approach 1:
The system performs self-supervised pretraining on large volumes of unlabeled documents to learn general document representations before fine-tuning on a small labeled dataset. This preliminary action on unlabeled data reduces the training time required for achieving high accuracy on labeled data, as the model already possesses general document understanding from the pretraining phase
Solution Approach 2:
The system applies different training strategies to different parts of the training process: self-supervised learning on unlabeled data for general representations, and supervised fine-tuning on labeled data for specific classification tasks. This local quality approach optimizes training efficiency by matching the training method to the data type and task requirements, reducing overall training time while maintaining accuracy
Data Source
AI summary
A data processing system implements a method for training machine learning modes, including receiving a set of one or more unlabeled documents associated one or more first categories of documents to be used to train machine learning models to analyze the one or more unlabeled documents, and fine-tuning a first machine learning model and a second machine learning model based on the one or more unlabeled document to enable the first machine learning model to determine a semantic representation of the one or more first categories of document, and to enable the second machine learning model to classify the semantic representations according to the one or more first categories of documents, the first machine learning model and the second machine learning model having been trained using first unlabeled training data including a second plurality of categories of documents that do not include the one or more first categories of documents.