@inproceedings{morton-warstadt-2026-closing,
    title = "Closing the Gap: Robust Multilingual Coreference Resolution with {DA}gger",
    author = "Morton, Thomas  and
      Warstadt, Alex",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Ogrodniczuk, Maciej  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Nov{\'a}k, Michal  and
      Li, Chuyuan  and
      Strube, Michael  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference ({CODI}-{CRAC} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.codi-1.28/",
    pages = "217--221",
    ISBN = "979-8-89176-400-2",
    abstract = "We present DAggerCoref, our submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. DAggerCoref is a three-stage cascade built on XLM-RoBERTa-large: a gap classifier for zero pronoun detection, a mention head classifier, and a coarse-to-fine antecedent scorer. Our central contribution is applying DAgger (Ross et al., 2011) to coreference resolution: after training the antecedent scorer on gold mentions, we fine-tune on a 50/50 mix of gold and pipeline-predicted mentions, closing the train/test distribution mismatch and improving development set macro CoNLL F1 by 1.10 points. We also introduce Otsu adaptive thresholding for zero pronoun detection, which matches gold-tuned per-dataset thresholds without requiring any gold supervision. Our system achieves a macro CoNLL F1 of 67.56 on the official test set across 27 datasets and 19 languages"
}