  Document-level relation extraction aims to discover relations between
entities across a whole document. How to build the dependency of entities from
different sentences in a document remains to be a great challenge. Current
approaches either leverage syntactic trees to construct document-level graphs
or aggregate inference information from different sentences. In this paper, we
build cross-sentence dependencies by inferring compositional relations between
inter-sentence mentions. Adopting aggressive linking strategy, intermediate
relations are reasoned on the document-level graphs by mention convolution. We
further notice the generalization problem of NA instances, which is caused by
incomplete annotation and worsened by fully-connected mention pairs. An
improved ranking loss is proposed to attend this problem. Experiments show the
connections between different mentions are crucial to document-level relation
extraction, which enables the model to extract more meaningful higher-level
compositional relations.
