Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
This work addresses efficiency in business operations by enabling information extraction from documents like regulatory filings and lease agreements, though it is incremental as it applies an existing method to new data.
The paper tackled the problem of automatically extracting content from domain-specific business documents like contracts and filings by adapting BERT for sequence labeling, achieving reasonable accuracy with less than 100 annotated documents.
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than 100 documents) are sufficient to achieve reasonable accuracy. We integrate our models into an end-to-end cloud platform that provides both an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect model outputs.