Papers › Improving Sentence-Level Relation Extraction through Curriculum Learning
Improving Sentence-Level Relation Extraction through Curriculum Learning
Seongsik Park, Harksoo Kim
Sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence. The sentence-level relation extraction corpus often contains data that are difficult for the model to infer or noise data. In this paper, we propose a curriculum learning-based relation extraction model that splits data by difficulty and utilizes them for learning. In the experiments with the representative sentence-level relation extraction datasets, TACRED and Re-TACRED, the proposed method obtained an F1-score of 75.0% and 91.4% respectively, which are the state-of-the-art 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 | Re-TACRED | EXOBRAIN | F1 | 91.4 | #1 of 9 | Archive leaderboard | report |
| Relation Extraction | TACRED | EXOBRAIN | F1 | 75.0 | #8 of 40 | 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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