  Extracting relations across large text spans has been relatively
underexplored in NLP, but it is particularly important for high-value domains
such as biomedicine, where obtaining high recall of the latest findings is
crucial for practical applications. Compared to conventional information
extraction confined to short text spans, document-level relation extraction
faces additional challenges in both inference and learning. Given longer text
spans, state-of-the-art neural architectures are less effective and
task-specific self-supervision such as distant supervision becomes very noisy.
In this paper, we propose decomposing document-level relation extraction into
relation detection and argument resolution, taking inspiration from Davidsonian
semantics. This enables us to incorporate explicit discourse modeling and
leverage modular self-supervision for each sub-problem, which is less
noise-prone and can be further refined end-to-end via variational EM. We
conduct a thorough evaluation in biomedical machine reading for precision
oncology, where cross-paragraph relation mentions are prevalent. Our method
outperforms prior state of the art, such as multi-scale learning and graph
neural networks, by over 20 absolute F1 points. The gain is particularly
pronounced among the most challenging relation instances whose arguments never
co-occur in a paragraph.
