Papers › Relation Classification as Two-way Span-Prediction
Relation Classification as Two-way Span-Prediction
Amir DN Cohen, Shachar Rosenman, Yoav Goldberg
The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as span-prediction (SP) problem, similar to Question answering (QA). We present a span-prediction based system for RC and evaluate its performance compared to the embedding based system. We demonstrate that the supervised SP objective works significantly better then the standard classification based objective. We achieve state-of-the-art results on the TACRED and SemEval task 8 datasets.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | SemEval-2010 Task-8 | SP | F1 | 91.9 | #1 of 31 | Archive leaderboard | report |
| Relation Extraction | TACRED | Relation Reduction | F1 | 74.8 | #9 of 40 | Archive leaderboard | report |
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