Papers › Sequence Generation with Label Augmentation for Relation Extraction
Sequence Generation with Label Augmentation for Relation Extraction
Bo Li, Dingyao Yu, Wei Ye, Jinglei Zhang, Shikun Zhang
Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | Google RE | RELA | F1 | 93.9 | #1 of 1 | Archive leaderboard | report |
| Relation Extraction | SemEval-2010 Task-8 | RELA | F1 | 90.4 | #6 of 31 | Archive leaderboard | report |
| Relation Extraction | TACRED | RELA | F1 | 71.2 | #20 of 40 | Archive leaderboard | report |
| Relation Extraction | sciERC-sent | RELA | F1 | 90.3 | #1 of 1 | 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.
Methods
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