Papers › REBEL: Relation Extraction By End-to-end Language generation
REBEL: Relation Extraction By End-to-end Language generation
Pere-Lluis Huguet Cabot, Roberto Navigli
Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model's flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.
Code
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Tasks
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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Joint Entity and Relation Extraction | DocRED | REBEL+pretraining | Relation F1 | 47.1 | #1 of 6 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | DocRED | REBEL | Relation F1 | 41.8 | #2 of 6 | Archive leaderboard | report |
| Relation Extraction | Adverse Drug Events (ADE) Corpus | REBEL (including overlapping entities) | RE+ Macro F1 | 82.2 | #6 of 15 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | REBEL | RE+ Macro F1 | 76.65 | #1 of 16 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | REBEL | RE+ Micro F1 | 75.4 | #1 of 16 | Archive leaderboard | report |
| Relation Extraction | NYT | REBEL (no pre-training) | F1 | 93.1 | #5 of 8 | Archive leaderboard | report |
| Relation Extraction | Re-TACRED | REBEL (no entity type marker) | F1 | 90.4 | #4 of 9 | 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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