@inproceedings{ploeger-etal-2025-tokenization,
    title = "Tokenization on Trial: {The} Case of {Kalaallisut}{--}{Danish} Legal Machine Translation",
    author = "Ploeger, Esther  and
      Saucedo, Paola  and
      Bjerva, Johannes  and
      Kristensen-McLachlan, Ross Deans  and
      Lent, Heather",
    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-1.52/",
    pages = "480--491",
    ISBN = "978-9908-53-109-0",
    abstract = "The strengths of subword tokenization have been widely demonstrated when applied to higher-resourced, morphologically simple languages. However, it is not self-evident that these results transfer to lower-resourced, morphologically complex languages. In this work, we investigate the influence of different subword segmentation techniques on machine translation between Danish and Kalaallisut, the official language of Greenland. We present the first semi-manually aligned parallel corpus for this language pair, and use it to compare subwords from unsupervised tokenizers and morphological segmenters. We find that Unigram-based segmentation both preserves morphological boundaries and handles out-of-vocabulary words adequately, but that this does not directly correspond to superior translation quality. We hope that our findings lay further groundwork for future efforts in neural machine translation for Kalaallisut."
}