@inproceedings{vamvas-2024-thesis,
    title = "Thesis: Model-based Evaluation of Multilinguality",
    author = "Vamvas, Jannis",
    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/ingest-emnlp/2024.eamt-1.4/",
    pages = "6--7",
    abstract = "The aim of this thesis was to extend the methodological toolbox for evaluating the ability of natural language processing systems to handle multiple languages. Neural machine translation (NMT) took the central role in this endeavour: NMT is inherently cross-lingual, and multilingual NMT systems, which translate from many source languages into many target languages, embody the concept of multilinguality in a very tangible way. In addition, NMT and specifically the perplexity of NMT systems can themselves be used as a tool for evaluating multilinguality."
}