@inproceedings{mcgill-etal-2024-bootstrapping,
    title = "Bootstrapping Pre-trained Word Embedding Models for Sign Language Gloss Translation",
    author = "McGill, Euan  and
      Chiruzzo, Luis  and
      Saggion, Horacio",
    editor = "Scarton, Carolina  and
      Prescott, Charlotte  and
      Bayliss, Chris  and
      Oakley, Chris  and
      Wright, Joanna  and
      Wrigley, Stuart  and
      Song, Xingyi  and
      Gow-Smith, Edward  and
      Bawden, Rachel  and
      S{\'a}nchez-Cartagena, V{\'i}ctor M  and
      Cadwell, Patrick  and
      Lapshinova-Koltunski, Ekaterina  and
      Cabarr{\~a}o, Vera  and
      Chatzitheodorou, Konstantinos  and
      Nurminen, Mary  and
      Kanojia, Diptesh  and
      Moniz, Helena",
    booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
    month = jun,
    year = "2024",
    address = "Sheffield, UK",
    publisher = "European Association for Machine Translation (EAMT)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.eamt-1.13/",
    pages = "116--132",
    abstract = "This paper explores a novel method to modify existing pre-trained word embedding models of spoken languages for Sign Language glosses. These newly-generated embeddings are described, visualised, and then used in the encoder and/or decoder of models for the Text2Gloss and Gloss2Text task of machine translation. In two translation settings (one including data augmentation-based pre-training and a baseline), we find that bootstrapped word embeddings for glosses improve translation across four Signed/spoken language pairs. Many improvements are statistically significant, including those where the bootstrapped gloss embedding models are used.Languages included: American Sign Language, Finnish Sign Language, Spanish Sign Language, Sign Language of The Netherlands."
}