@inproceedings{hackenbuchner-etal-2024-automatic,
    title = "Automatic detection of (potential) factors in the source text leading to gender bias in machine translation",
    author = "Hackenbuchner, Jani{\c{c}}a  and
      Tezcan, Arda  and
      Daems, Joke",
    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
      Forcada, Mikel  and
      Moniz, Helena",
    booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)",
    month = jun,
    year = "2024",
    address = "Sheffield, UK",
    publisher = "European Association for Machine Translation (EAMT)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.eamt-2.14/",
    pages = "27--28",
    abstract = "This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model."
}