@inproceedings{nieminen-etal-2025-incorporating,
    title = "Incorporating Target Fuzzy Matches into Neural Fuzzy Repair",
    author = {Nieminen, Tommi  and
      Tiedemann, J{\"o}rg  and
      Virpioja, Sami},
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nodalida-1.44/",
    pages = "408--418",
    ISBN = "978-9908-53-109-0",
    abstract = "Neural fuzzy repair (NFR) is a simple implementation of retrieval-augmented translation (RAT), based on data augmentation. In NFR, a translation database is searched for translation examples where the source sentence is similar to the sentence being translated, and the target side of the example is concatenated with the source sentences. We experiment with introducing retrieval that is based on target similarity to NFR during training. The results of our experiments confirm that including target similarity matches during training supplements source similarity matches and leads to better translations at translation time."
}