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A Masked Image Reconstruction Network for Document-level Relation Extraction
After that, we look on the entity-pair matrix as an image and then randomly mask it and restore it through an inference module to capture the correlations between the relationships.
Ranked #3 on
Relation Extraction
on GDA
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction
Document-level relation extraction (RE), which requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations, is more challenging than sentence-level RE.
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction
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.
Ranked #2 on
Relation Extraction
on CDR
