@inproceedings{zesch-etal-2025-transformer,
    title = "Transformer-Based Real-Word Spelling Error Feedback with Configurable Confusion Sets",
    author = "Zesch, Torsten  and
      Gardner, Dominic  and
      Bexte, Marie",
    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-1.29/",
    pages = "375--383",
    ISBN = "979-8-89176-270-1",
    abstract = "Real-word spelling errors (RWSEs) pose special challenges for detection methods, as they `hide' in the form of another existing word and in many cases even fit in syntactically. We present a modern Transformer-based implementation of earlier probabilistic methods based on confusion sets and show that RWSEs can be detected with a good balance between missing errors and raising too many falsealarms. The confusion sets are dynamically configurable, allowing teachers to easily adjust which errors trigger feedback."
}