@inproceedings{zhou-de-cruys-2025-non,
    title = "Non-autoregressive Modeling for Sign-gloss to Texts Translation",
    author = "Zhou, Fan  and
      de Cruys, Tim Van",
    editor = "Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Rubino, Raphael  and
      Sennrich, Rico  and
      Farinha, Ana C.  and
      Gaido, Marco  and
      Daems, Joke  and
      Kenny, Dorothy  and
      Moniz, Helena  and
      Szoc, Sara",
    booktitle = "Proceedings of Machine Translation Summit XX: Volume 1",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.mtsummit-1.17/",
    pages = "220--230",
    ISBN = "978-2-9701897-0-1",
    abstract = "Automatic sign language translation has seen significant advancements, driven by progress in computer vision and natural language processing. While end to end sign-to-text translation systems are available, many systems still rely on a gloss-based representation{--}an intermediate symbolic representation that functions as a bridge between sign language and its written counterpart. This paper focuses on the gloss-to-text (gloss2text) task, a key step in the sign-to-text translation pipeline, which has traditionally been addressed using autoregressive (AR) modeling approaches. In this study, we propose the use of non-autoregressive (NAR) modeling techniques, including non-autoregressive Transformer (NAT) and diffusion models, tailored to the unique characteristics of gloss2text. Specifically, we introduce PointerLevT, a novel NAT-based model designed to enhance performance in this task. Our experiments demonstrate that NAR models achieve higher accuracy than pre-trained AR models with less data, while also matching the performance of fine-tuned AR models such as mBART. Furthermore, we evaluate inference speed and find that NAR models benefit from parallel generation, resulting in faster inference. However, they require more time to achieve an optimal balance between accuracy and speed, particularly in the multistep denoising process of diffusion models."
}