Papers › SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
Shuang Zeng, Yuting Wu, Baobao Chang
Document-level relation extraction has attracted much attention in recent years. It is usually formulated as a classification problem that predicts relations for all entity pairs in the document. However, previous works indiscriminately represent intra- and inter-sentential relations in the same way, confounding the different patterns for predicting them. Besides, they create a document graph and use paths between entities on the graph as clues for logical reasoning. However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph. Thus many cases of logical reasoning cannot be covered. This paper proposes an effective architecture, SIRE, to represent intra- and inter-sentential relations in different ways. We design a new and straightforward form of logical reasoning module that can cover more logical reasoning chains. Experiments on the public datasets show SIRE outperforms the previous state-of-the-art methods. Further analysis shows that our predictions are reliable and explainable. Our code is available at https://github.com/DreamInvoker/SIRE.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | DocRED | SIRE-BERT-base | F1 | 62.05 | #19 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | SIRE-BERT-base | Ign F1 | 60.18 | #19 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | SIRE-GloVe | F1 | 55.96 | #49 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | SIRE-GloVe | Ign F1 | 54.04 | #49 of 62 | Archive leaderboard | report |
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