  Document-level relation extraction (RE) aims to identify relations between
two entities in a given document. Compared with its sentence-level counterpart,
document-level RE requires complex reasoning. Previous research normally
completed reasoning through information propagation on the mention-level or
entity-level document-graph, but rarely considered reasoning at the
entity-pair-level.In this paper, we propose a novel model, called Densely
Connected Criss-Cross Attention Network (Dense-CCNet), for document-level RE,
which can complete logical reasoning at the entity-pair-level. Specifically,
the Dense-CCNet performs entity-pair-level logical reasoning through the
Criss-Cross Attention (CCA), which can collect contextual information in
horizontal and vertical directions on the entity-pair matrix to enhance the
corresponding entity-pair representation. In addition, we densely connect
multiple layers of the CCA to simultaneously capture the features of single-hop
and multi-hop logical reasoning.We evaluate our Dense-CCNet model on three
public document-level RE datasets, DocRED, CDR, and GDA. Experimental results
demonstrate that our model achieves state-of-the-art performance on these three
datasets.
