@inproceedings{schaller-etal-2025-dont,
    title = "Don{'}t Score too Early! Evaluating Argument Mining Models on Incomplete Essays",
    author = "Schaller, Nils-Jonathan  and
      Ding, Yuning  and
      Jansen, Thorben  and
      Horbach, Andrea",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.27/",
    pages = "345--355",
    ISBN = "979-8-89176-270-1",
    abstract = "Students' argumentative writing benefits from receiving automated feedback, particularly throughout the writing process. While Argument Mining (AM) technology shows promise for delivering automated feedback on argumentative structures, existing systems are frequently trained on completed essays, providing rich context information and raising concerns about their usefulness for offering writing support on incomplete texts during the writing process. This study evaluates the robustness of AM algorithms on artificially fragmented learner texts from two large-scale corpora of secondary school essays: the German DARIUS corpus and the English PERSUADE corpus. Our analysis reveals that token-level sequence-tagging methods, while highly effective on complete essays, suffer significantly when context is limited or misleading. Conversely, sentence-level classifiers maintain relative stability under such conditions. We show that deliberately training AM models on fragmented input substantially mitigates these context-related weaknesses, enabling AM systems to support dynamic educational writing scenarios better."
}