Papers › HIN: Hierarchical Inference Network for Document-Level Relation Extraction
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
Hengzhu Tang, Yanan Cao, Zhen-Yu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin
Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.
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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 | HIN-BERT-base | F1 | 55.60 | #51 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | HIN-BERT-base | Ign F1 | 53.70 | #51 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | HIN-GloVe | F1 | 53.30 | #56 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | HIN-GloVe | Ign F1 | 51.15 | #56 of 62 | 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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