{"url":"/dataset/re-docred","name":"Re-DocRED","full_name":"Revisiting Document Level Relation Extraction","description_markdown":"The Re-DocRED Dataset resolved the following problems of DocRED:\r\n\r\n1. Resolved the incompleteness problem by supplementing large amounts of relation triples.\r\n2. Addressed the logical inconsistencies in DocRED.\r\n3. Corrected the coreferential errors within DocRED.","description_withheld":null,"homepage":"https://github.com/tonytan48/Re-DocRED","introduced_date":"2022-05-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/revisiting-docred-addressing-the-overlooked","title":"Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction","first_author":"Qingyu Tan","url":null},"license":null,"modalities":[],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Document-level Relation Extraction","url":"/task/document-level-relation-extraction","datasets_with_task":"/datasets/task/document-level-relation-extraction"},{"name":"Document-level RE with incomplete labeling","url":"/task/document-level-re-with-incomplete-labeling","datasets_with_task":"/datasets/task/document-level-re-with-incomplete-labeling"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ReDocRED","Re-DocRED"],"data_loaders":[],"num_papers_in_archive":34,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-redocred","task":"Relation Extraction","dataset_variant":"ReDocRED","rows":8,"metrics":["F1","Ign F1"],"first_row_in_archive_order":{"model":"TTM-RE","paper":"/paper/ttm-re-memory-augmented-document-level","metrics":{"F1":"84.01","Ign F1":"83.11"},"code_links":[{"title":"chufangao/ttm-re","url":"https://github.com/chufangao/ttm-re"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/document-level-re-with-incomplete-labeling-on-1","task":"Document-level RE with incomplete labeling","dataset_variant":"Re-DocRED","rows":2,"metrics":["F1","Ign F1"],"first_row_in_archive_order":{"model":"SSR-PU","paper":"/paper/a-unified-positive-unlabeled-learning","metrics":{"F1":"59.50","Ign F1":"58.68"},"code_links":[{"title":"www-ye/ssr-pu","url":"https://github.com/www-ye/ssr-pu"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/document-level-relation-extraction-on-re","task":"Document-level Relation Extraction","dataset_variant":"Re-DocRED","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"VaeDiff-DocRE","paper":"/paper/vaediff-docre-end-to-end-data-augmentation","metrics":{"F1":"0.7903"},"code_links":[{"title":"khaitran22/vaediff-docre","url":"https://github.com/khaitran22/vaediff-docre"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vaediff-docre-end-to-end-data-augmentation","title":"VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction","date":"2024-12-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ttm-re-memory-augmented-document-level","title":"TTM-RE: Memory-Augmented Document-Level Relation Extraction","date":"2024-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adaptive-hinge-balance-loss-for-document","title":"Adaptive Hinge Balance Loss for Document-Level Relation Extraction","date":"2023-12-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dreeam-guiding-attention-with-evidence-for","title":"DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction","date":"2023-02-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-unified-positive-unlabeled-learning","title":"A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling","date":"2022-10-17","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with-4","title":"Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation","date":"2022-03-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-as","title":"Document-level Relation Extraction as Semantic Segmentation","date":"2021-06-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/an-end-to-end-model-for-entity-level-relation","title":"An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning","date":"2021-02-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with","title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling","date":"2020-10-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}