  Building document-grounded dialogue systems have received growing interest as
documents convey a wealth of human knowledge and commonly exist in enterprises.
Wherein, how to comprehend and retrieve information from documents is a
challenging research problem. Previous work ignores the visual property of
documents and treats them as plain text, resulting in incomplete modality. In
this paper, we propose a Layout-aware document-level Information Extraction
dataset, LIE, to facilitate the study of extracting both structural and
semantic knowledge from visually rich documents (VRDs), so as to generate
accurate responses in dialogue systems. LIE contains 62k annotations of three
extraction tasks from 4,061 pages in product and official documents, becoming
the largest VRD-based information extraction dataset to the best of our
knowledge. We also develop benchmark methods that extend the token-based
language model to consider layout features like humans. Empirical results show
that layout is critical for VRD-based extraction, and system demonstration also
verifies that the extracted knowledge can help locate the answers that users
care about.
