  Compared with traditional sentence-level relation extraction, document-level
relation extraction is a more challenging task where an entity in a document
may be mentioned multiple times and associated with multiple relations.
However, most methods of document-level relation extraction do not distinguish
between mention-level features and entity-level features, and just apply simple
pooling operation for aggregating mention-level features into entity-level
features. As a result, the distinct semantics between the different mentions of
an entity are overlooked. To address this problem, we propose RSMAN in this
paper which performs selective attentions over different entity mentions with
respect to candidate relations. In this manner, the flexible and
relation-specific representations of entities are obtained which indeed benefit
relation classification. Our extensive experiments upon two benchmark datasets
show that our RSMAN can bring significant improvements for some backbone models
to achieve state-of-the-art performance, especially when an entity have
multiple mentions in the document.
