Papers by Navneet Potti
Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models (2023.emnlp-main)
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| Challenge: | a key bottleneck in developing automatic extraction models for visually rich documents is the cost of acquiring labeled documents. |
| Approach: | They propose selective labeling to provide "yes/no" labels for candidate extractions predicted by a model trained on partially labeled documents. |
| Outcome: | The proposed method reduces the cost of acquiring labeled data by 10 with a negligible loss in accuracy. |
Representation Learning for Information Extraction from Form-like Documents (2020.acl-main)
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| Challenge: | Form-like documents like invoices, purchase orders, tax forms and insurance quotes are common in day-to-day business workflows, but current techniques for processing them largely still employ manual effort or brittle and error-prone heuristics for extraction. |
| Approach: | They 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. |
| Outcome: | The proposed system generates extraction candidates based on neighboring words in the document and is interpretable, as shown using loss cases. |