  Document-level relation extraction requires integrating information within
and across multiple sentences of a document and capturing complex interactions
between inter-sentence entities. However, effective aggregation of relevant
information in the document remains a challenging research question. Existing
approaches construct static document-level graphs based on syntactic trees,
co-references or heuristics from the unstructured text to model the
dependencies. Unlike previous methods that may not be able to capture rich
non-local interactions for inference, we propose a novel model that empowers
the relational reasoning across sentences by automatically inducing the latent
document-level graph. We further develop a refinement strategy, which enables
the model to incrementally aggregate relevant information for multi-hop
reasoning. Specifically, our model achieves an F1 score of 59.05 on a
large-scale document-level dataset (DocRED), significantly improving over the
previous results, and also yields new state-of-the-art results on the CDR and
GDA dataset. Furthermore, extensive analyses show that the model is able to
discover more accurate inter-sentence relations.
