@inproceedings{braud-etal-2025-disrpt,
    title = "The {DISRPT} 2025 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification",
    author = "Braud, Chlo{\'e}  and
      Zeldes, Amir  and
      Li, Chuyuan  and
      Liu, Yang Janet  and
      Muller, Philippe",
    editor = "Braud, Chlo{\'e}  and
      Liu, Yang Janet  and
      Muller, Philippe  and
      Zeldes, Amir  and
      Li, Chuyuan",
    booktitle = "Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.disrpt-1.1/",
    pages = "1--20",
    ISBN = "979-8-89176-344-9",
    abstract = "In 2025, we held the fourth iteration of the DISRPT Shared Task (Discourse Relation Parsing and Treebanking) dedicated to discourse parsing across formalisms. Following the success of the 2019, 2021, and 2023 tasks on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification, this iteration added 13 new datasets, including three new languages (Czech, Polish, Nigerian Pidgin) and two new frameworks: the ISO framework and Enhanced Rhetorical Structure Theory, in addition to the previously included frameworks: RST, SDRT, DEP, and PDTB. In this paper, we review the data included in DISRPT 2025, which covers 39 datasets across 16 languages, survey and compare submitted systems, and report on system performance on each task for both treebanked and plain-tokenized versions of the data. The best systems obtain a mean accuracy of 71.19{\%} for relation classification, a mean F1 of 91.57 (Treebanked Track) and 87.38 (Plain Track) for segmentation, and a mean F1 of 81.53 (Treebanked Track) and 79.92 (Plain Track) for connective identification. The data and trained models of several participants can be found at https://huggingface.co/multilingual-discourse-hub."
}