James Bradley Wendt


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Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models
Yichao Zhou | James Bradley Wendt | Navneet Potti | Jing Xie | Sandeep Tata
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Building automatic extraction models for visually rich documents like invoices, receipts, bills, tax forms, etc. has received significant attention lately. A key bottleneck in developing extraction models for new document types is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy. In this paper, we propose selective labeling as a solution to this problem. The key insight is to simplify the labeling task to provide “yes/no” labels for candidate extractions predicted by a model trained on partially labeled documents. We combine this with a custom active learning strategy to find the predictions that the model is most uncertain about. We show through experiments on document types drawn from 3 different domains that selective labeling can reduce the cost of acquiring labeled data by 10× with a negligible loss in accuracy.


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Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Prasad Majumder | Navneet Potti | Sandeep Tata | James Bradley Wendt | Qi Zhao | Marc Najork
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains but are also interpretable, as we show using loss cases.