Papers › Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang
Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. The adaptive thresholding replaces the global threshold for multi-label classification in the prior work with a learnable entities-dependent threshold. The localized context pooling directly transfers attention from pre-trained language models to locate relevant context that is useful to decide the relation. We experiment on three document-level RE benchmark datasets: DocRED, a recently released large-scale RE dataset, and two datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also significantly outperforms existing models on both CDR and GDA.
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Tasks
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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 | SciBERT-ATLOPBASE | F1 | 69.4 | #6 of 10 | Archive leaderboard | report |
| Relation Extraction | DocRED | ATLOP-RoBERTa-large | F1 | 63.40 | #9 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | ATLOP-RoBERTa-large | Ign F1 | 61.39 | #9 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | ATLOP-BERT-base | F1 | 61.30 | #26 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | ATLOP-BERT-base | Ign F1 | 59.31 | #26 of 62 | Archive leaderboard | report |
| Relation Extraction | GDA | SciBERT-ATLOPBASE | F1 | 83.9 | #7 of 9 | Archive leaderboard | report |
| Relation Extraction | ReDocRED | ATLOP | F1 | 77.56 | #7 of 8 | Archive leaderboard | report |
| Relation Extraction | ReDocRED | ATLOP | Ign F1 | 76.82 | #7 of 8 | 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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