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Workshop
Document Intelligence
Nigel Duffy · Rama Akkiraju · Tania Bedrax Weiss · Paul Bennett · Hamid Reza Motahari-Nezhad

Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ West 208 + 209
Event URL: https://sites.google.com/view/di2019 »

Business documents are central to the operation of business. Such documents include sales agreements, vendor contracts, mortgage terms, loan applications, purchase orders, invoices, financial statements, employment agreements and a wide many more. The information in such business documents is presented in natural language, and can be organized in a variety of ways from straight text, multi-column formats, and a wide variety of tables. Understanding these documents is made challenging due to inconsistent formats, poor quality scans and OCR, internal cross references, and complex document structure. Furthermore, these documents often reflect complex legal agreements and reference, explicitly or implicitly, regulations, legislation, case law and standard business practices.
The ability to read, understand and interpret business documents, collectively referred to here as “Document Intelligence”, is a critical and challenging application of artificial intelligence (AI) in business. While a variety of research has advanced the fundamentals of document understanding, the majority have focused on documents found on the web which fail to capture the complexity of analysis and types of understanding needed across business documents. Realizing the vision of document intelligence remains a research challenge that requires a multi-disciplinary perspective spanning not only natural language processing and understanding, but also computer vision, knowledge representation and reasoning, information retrieval, and more -- all of which have been profoundly impacted and advanced by neural network-based approaches and deep learning in the last few years.
We propose to organize a workshop for AI researchers, academics and industry practitioners to discuss the opportunities and challenges for document intelligence.

Author Information

Nigel Duffy (EY)
Rama Akkiraju (IBM Research - Almaden)
Tania Bedrax Weiss (Google)
Paul Bennett (Microsoft Research)
Hamid Reza Motahari-Nezhad (EY AI Lab, USA)

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