@inproceedings{heng-etal-2026-re2,
    title = "Re2-{D}oc{RED}: Revisiting Revisited-{D}oc{RED} for Joint Entity and Relation Extraction",
    author = "Heng, Chen Kim  and
      Tong, Shao Wen  and
      Sheng, Julian Wong Wei",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.213/",
    pages = "4585--4621",
    ISBN = "979-8-89176-380-7",
    abstract = "Document-level Joint Entity and Relation Extraction (JERE) benchmarks such as DocRED, Re-DocRED, and DocGNRE suffer from pervasive False Negatives (FN), undermining training and evaluation. In this paper, we introduce SiftingLogic {--} a training-free annotation pipeline that leverages LLMs with user-specifiable reasoning, enriched inverse/co-occurring relation schemas, and novel entity-level constraints to systematically address FN gaps. Applying SiftingLogic and our enriched schema of inverse and co-occurring relations, we add 29,580 verified triplets to Re-DocRED (train/dev, +27{\%}) and over 9,700 verified triplets to DocGNRE test (+49.89{\%}), yielding the enhanced Re$^2$-DocRED dataset. Beyond English datasets, we also apply our SiftingLogic to REDFM Mandarin test set, resulting in a significant increase in triplets from 663 to 1,391 (+109.8{\%}) demonstrating our pipeline{'}s generalisability across languages and datasets. Our experiments show that recall scores of models trained on existing public datasets drop notably on our revised splits, whereas our enriched training set mitigates this, underscoring persistent FN gaps and motivating our proposed SiftingLogic and Re$^2$-DocRED. To facilitate further research and reproducibility of our work, the Re$^2$-DocRED dataset is released at \url{https://github.com/klassessg/re2-docred}."
}