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.
In addition to invited talks and open discussions on topics related to document intelligence, the workshop program will include a poster session which provides an opportunity to present peer-reviewed work on the topic of Document Intelligence. We are soliciting submissions of short research and vision papers of PDF format from 2 to 4 pages for presentation at the Poster session, as follows:
2-page limit: Abstracts of already published contributions in Top-Tier venues (with a focus on parts relevant topics to document intelligence), description of datasets, position and vision papers, as well as papers describing industry, scientific or theoretical challenges
4-page limit: Original research contributions, or abstracts of papers rejected/recycled papers from NeurIPS or other top-tier venues (not published contributions). The research contributions may discuss technical challenges of reading and interpreting business documents and present research results.
It is expected that one of the authors of accepted contributions will attend the workshop to present the work, along with a poster, in the workshop's Poster Session. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals.
The topics of interest to the workshop include but are not limited to the following:
Document modeling, and representations
Document structure and layout learning
Cleansing and image enhancement techniques for scanned documents
Information extraction from text, and semi-structured documents
Linguistic analysis of document content
Natural language reasoning, and inference
Question answering on business documents
Semantic understanding of document content
Document search, and clustering
Handwritten recognition in business documents
Table identification and extraction from business documents
Chart learning, and understanding
Domain-specific document understanding
Knowledge representation for business documents
Multi-lingual document understanding methods and frameworks
Integrated syntax and semantic approaches for document understanding
Transfer learning methods for business document reading and understanding
Important Dates
Paper Submission Deadline: September 9, 2019
Paper Notification Date: October 1, 2019
Workshop Date: December 13 or 14, 2019 (TBD)
Submission URL
https://openreview.net/group?id=NeurIPS.cc/2019/Workshop/Document_Intell...
Workshop Organizing Committee
Tania Bedrax Weiss (Google)
Paul Bennett (Microsoft)
Nigel Duffy (EY)
Rama Akkiraju (IBM)
Program Committee Chair
Hamid Motahari (EY)
Program Committee Members
Ryan McDonald (Google)
Ashok Popat (Google)
Michael Witbrock (The University of Auckland)
Mohit Bansal (UNC Chapel Hill)
Dan Goldwasser (Purdue University)
Dan Tecuci (EY)
Peter Yeh (Nuance)
Douglas Burdick (IBM Research)
Yunyao Li (IBM Research)
Vibha Sinha (Facebook)
James Fan (Google)
DooSoon Kim (Adobe)
Laura Chiticariu (IBM Watson)
Shivakumar Vaithyanathan (Adobe)