Towards Complex Document Understanding By Discrete Reasoning. (arXiv:2207.11871v2 [cs.CV] UPDATED)

Document Visual Question Answering (VQA) aims to understand visually-rich
documents to answer questions in natural language, which is an emerging
research topic for both Natural Language Processing and Computer Vision. In
this work, we introduce a new Document VQA dataset, named TAT-DQA, which
consists of 3,067 document pages comprising semi-structured table(s) and
unstructured text as well as 16,558 question-answer pairs by extending the
TAT-QA dataset. These documents are sampled from real-world financial reports
and contain lots of numbers, which means …

arxiv document understanding reasoning understanding