@inproceedings{kocmi-etal-2024-error,
    title = "Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation",
    author = "Kocmi, Tom  and
      Zouhar, Vil{\'e}m  and
      Avramidis, Eleftherios  and
      Grundkiewicz, Roman  and
      Karpinska, Marzena  and
      Popovi{\'c}, Maja  and
      Sachan, Mrinmaya  and
      Shmatova, Mariya",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wmt-1.131/",
    doi = "10.18653/v1/2024.wmt-1.131",
    pages = "1440--1453",
    abstract = "High-quality Machine Translation (MT) evaluation relies heavily on human judgments.Comprehensive error classification methods, such as Multidimensional Quality Metrics (MQM), are expensive as they are time-consuming and can only be done by experts, whose availability may be limited especially for low-resource languages.On the other hand, just assigning overall scores, like Direct Assessment (DA), is simpler and faster and can be done by translators of any level, but is less reliable.In this paper, we introduce Error Span Annotation (ESA), a human evaluation protocol which combines the continuous rating of DA with the high-level error severity span marking of MQM.We validate ESA by comparing it to MQM and DA for 12 MT systems and one human reference translation (English to German) from WMT23. The results show that ESA offers faster and cheaper annotations than MQM at the same quality level, without the requirement of expensive MQM experts."
}