  In this paper, we consider the problem of organizing supporting documents
vital to U.S. work visa petitions, as well as responding to Requests For
Evidence (RFE) issued by the U.S.~Citizenship and Immigration Services (USCIS).
Typically, both processes require a significant amount of repetitive manual
effort. To reduce the burden of mechanical work, we apply machine learning
methods to automate these processes, with humans in the loop to review and edit
output for submission. In particular, we use an ensemble of image and text
classifiers to categorize supporting documents. We also use a text classifier
to automatically identify the types of evidence being requested in an RFE, and
used the identified types in conjunction with response templates and extracted
fields to assemble draft responses. Empirical results suggest that our approach
achieves considerable accuracy while significantly reducing processing time.
