@inproceedings{knowles-larkin-2023-long,
    title = "Long to reign over us: A Case Study of Machine Translation and a New Monarch",
    author = "Knowles, Rebecca  and
      Larkin, Samuel",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.412",
    doi = "10.18653/v1/2023.findings-acl.412",
    pages = "6589--6598",
    abstract = "Novel terminology and changes in terminology are often a challenge for machine translation systems. The passing of Queen Elizabeth II and the accession of King Charles III provide a striking example of translation shift in the real world, particularly in translation contexts that have ambiguity. Examining translation between French and English, we present a focused case-study of translations about King Charles III as produced both by publicly-available MT systems and by a neural machine translation system trained specifically on Canadian parliamentary text. We find that even in cases where human translators would have adequate context to disambiguate terms from the source language, machine translation systems do not always produce the expected output. Where we are able to analyze the training data, we note that this may represent artifacts in the data, raising important questions about machine translation updates in light of real world events.",
}

Novel terminology and changes in terminology are often a challenge for machine translation systems. The passing of Queen Elizabeth II and the accession of King Charles III provide a striking example of translation shift in the real world, particularly in translation contexts that have ambiguity. Examining translation between French and English, we present a focused case-study of translations about King Charles III as produced both by publicly-available MT systems and by a neural machine translation system trained specifically on Canadian parliamentary text. We find that even in cases where human translators would have adequate context to disambiguate terms from the source language, machine translation systems do not always produce the expected output. Where we are able to analyze the training data, we note that this may represent artifacts in the data, raising important questions about machine translation updates in light of real world events.
Knowles, Rebecca Larkin, Samuel
Long to reign over us: A Case Study of Machine Translation and a New Monarch