@inproceedings{dreano-etal-2024-exploration,
    title = "Exploration of the {C}ycle{GN} Framework for Low-Resource Languages",
    author = {Dreano, S{\"o}ren  and
      Molloy, Derek  and
      Murphy, Noel},
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wmt-1.66/",
    doi = "10.18653/v1/2024.wmt-1.66",
    pages = "756--761",
    abstract = "CycleGN is a Neural Machine Translation framework relying on the Transformer architecture. The foundational concept of our research posits that in an ideal scenario, retro-translations of generated translations should revert to the original source sentences. Consequently, a pair of models can be trained using a Cycle Consistency Loss only, with one model translating in one direction and the second model in the opposite direction."
}