DOCMASTER: A Unified Platform for Annotation, Training, & Inference in
  Document Question-Answering
Alex Nguyen Zilong Wang Jingbo Shang Dheeraj Mekala
The application of natural language processing models to PDF documents is
pivotal for various business applications yet the challenge of training models
for this purpose persists in businesses due to specific hurdles. These include
the complexity of working with PDF formats that necessitate parsing text and
layout information for curating training data and the lack of
privacy-preserving annotation tools. This paper introduces DOCMASTER, a unified
platform designed for annotating PDF documents, model training, and inference,
tailored to document question-answering. The annotation interface enables users
to input questions and highlight text spans within the PDF file as answers,
saving layout information and text spans accordingly. Furthermore, DOCMASTER
supports both state-of-the-art layout-aware and text models for comprehensive
training purposes. Importantly, as annotations, training, and inference occur
on-device, it also safeguards privacy. The platform has been instrumental in
driving several research prototypes concerning document analysis such as the AI
assistant utilized by University of California San Diego's (UCSD) International
Services and Engagement Office (ISEO) for processing a substantial volume of
PDF documents.