  We address the challenging problem of Natural Language Comprehension beyond
plain-text documents by introducing the TILT neural network architecture which
simultaneously learns layout information, visual features, and textual
semantics. Contrary to previous approaches, we rely on a decoder capable of
unifying a variety of problems involving natural language. The layout is
represented as an attention bias and complemented with contextualized visual
information, while the core of our model is a pretrained encoder-decoder
Transformer. Our novel approach achieves state-of-the-art results in extracting
information from documents and answering questions which demand layout
understanding (DocVQA, CORD, SROIE). At the same time, we simplify the process
by employing an end-to-end model.
