  In document-level relation extraction (DocRE), graph structure is generally
used to encode relation information in the input document to classify the
relation category between each entity pair, and has greatly advanced the DocRE
task over the past several years. However, the learned graph representation
universally models relation information between all entity pairs regardless of
whether there are relationships between these entity pairs. Thus, those entity
pairs without relationships disperse the attention of the encoder-classifier
DocRE for ones with relationships, which may further hind the improvement of
DocRE. To alleviate this issue, we propose a novel
encoder-classifier-reconstructor model for DocRE. The reconstructor manages to
reconstruct the ground-truth path dependencies from the graph representation,
to ensure that the proposed DocRE model pays more attention to encode entity
pairs with relationships in the training. Furthermore, the reconstructor is
regarded as a relationship indicator to assist relation classification in the
inference, which can further improve the performance of DocRE model.
Experimental results on a large-scale DocRE dataset show that the proposed
model can significantly improve the accuracy of relation extraction on a strong
heterogeneous graph-based baseline.
