Papers › Improving Document-level Relation Extraction via Contextualizing Mention...
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs
Ping Jiang;Xian-Ling Mao;Binbin Bian;Heyan Huang
Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair. To tackle the above two problems, in this paper, we propose a novel encoder-attender-aggregator model, which introduces two attenders between the encoder and aggregator. Specifically, a mutual attender is first employed on the selected head and tail mentions to efficiently produce contextualized mention representations. Then, an integration attender is utilized to weight the mention pairs of a target entity pair. Extensive experiments on two document-level RE datasets show that the proposed model performs better than the state-of-the-art baselines. Our codes are publicly available at "https://github.com/nefujiangping/EncAttAgg".
Code
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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 | EncAttAgg | F1 | 58.7 | #42 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | EncAttAgg | Ign F1 | 56.7 | #42 of 62 | Archive leaderboard | report |
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