@inproceedings{tran-etal-2026-vietmix,
    title = "{V}iet{M}ix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for {V}ietnamese-{E}nglish Code-Mixed Machine Translation",
    author = "Tran, Hieu  and
      Nguyen-Le, Phuong-Anh  and
      Nghiem, Huy  and
      Nguyen, Quang-Nhan  and
      Ai, Wei  and
      Carpuat, Marine",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.342/",
    pages = "7266--7284",
    ISBN = "979-8-89176-380-7",
    abstract = "Machine translation (MT) systems universally degrade when faced with code-mixed text. This problem is more acute for low-resource languages that lack dedicated parallel corpora. This work directly addresses this gap for Vietnamese-English, a language context characterized by challenges including orthographic ambiguity and the frequent omission of diacritics in informal text. We introduce VietMix, the first expert-translated, naturally occurring parallel corpus of Vietnamese-English code-mixed text. We establish VietMix{'}s utility by developing a data augmentation pipeline that leverages iterative fine-tuning and targeted filtering. Experiments show that models augmented with our data outperform strong back-translation baselines by up to +3.5 xCOMET points and improve zero-shot models by up to +11.9 points. Our work delivers a foundational resource for a challenging language pair and provides a validated, transferable framework for building and augmenting corpora in other low-resource settings."
}