Papers › A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction
Liang Zhang, Yidong Cheng
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | CDR | Dense-CCNet-SciBERTbase | F1 | 77.06 | #2 of 10 | Archive leaderboard | report |
| Relation Extraction | DocRED | Dense-CCNet-BERTbase | F1 | 62.55 | #13 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | Dense-CCNet-BERTbase | Ign F1 | 60.46 | #13 of 62 | Archive leaderboard | report |
| Relation Extraction | GDA | Dense-CCNet-SciBERTbase | F1 | 86.44 | #2 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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