  Document-level relation extraction (DocRE) involves identifying relations
between entities distributed in multiple sentences within a document. Existing
methods focus on building a heterogeneous document graph to model the internal
structure of an entity and the external interaction between entities. However,
there are two drawbacks in existing methods. On one hand, anaphor plays an
important role in reasoning to identify relations between entities but is
ignored by these methods. On the other hand, these methods achieve
cross-sentence entity interactions implicitly by utilizing a document or
sentences as intermediate nodes. Such an approach has difficulties in learning
fine-grained interactions between entities across different sentences,
resulting in sub-optimal performance. To address these issues, we propose an
Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the
widely-used datasets demonstrate that our model achieves a new state-of-the-art
performance.
