@inproceedings{nishida-etal-2026-probabilistic,
    title = "Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment",
    author = "Nishida, Shoto  and
      Matsui, Daiki  and
      Ninomiya, Takashi  and
      Goto, Isao  and
      Tamura, Akihiro",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.40/",
    pages = "528--534",
    ISBN = "979-8-89176-383-8",
    abstract = "This study proposes a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. Conventional neural machine translation typically employs subword segmentation models trained. However, since existing methods do not consider parallel relationships, inconsistencies in word segmentation between source and target languages may hinder translation model training. Our approach leverages direct modeling of subword correspondences in parallel corpora, thereby improving segmentation consistency across languages. Experiments across multiple machine translation tasks confirm that our proposed method improves translation accuracy for many tasks."
}