@inproceedings{avramidis-macketanz-2022-linguistically,
    title = "Linguistically Motivated Evaluation of Machine Translation Metrics Based on a Challenge Set",
    author = "Avramidis, Eleftherios  and
      Macketanz, Vivien",
    editor = {Koehn, Philipp  and
      Barrault, Lo{\"i}c  and
      Bojar, Ond{\v{r}}ej  and
      Bougares, Fethi  and
      Chatterjee, Rajen  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Fishel, Mark  and
      Fraser, Alexander  and
      Freitag, Markus  and
      Graham, Yvette  and
      Grundkiewicz, Roman  and
      Guzman, Paco  and
      Haddow, Barry  and
      Huck, Matthias  and
      Jimeno Yepes, Antonio  and
      Kocmi, Tom  and
      Martins, Andr{\'e}  and
      Morishita, Makoto  and
      Monz, Christof  and
      Nagata, Masaaki  and
      Nakazawa, Toshiaki  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Popel, Martin  and
      Turchi, Marco  and
      Zampieri, Marcos},
    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.wmt-1.45/",
    pages = "514--529",
    abstract = "We employ a linguistically motivated challenge set in order to evaluate the state-of-the-art machine translation metrics submitted to the Metrics Shared Task of the 7th Conference for Machine Translation. The challenge set includes about 20,000 items extracted from 145 MT systems for two language directions (German-English, English-German), covering more than 100 linguistically-motivated phenomena organized in 14 categories. The best performing metrics are YiSi-1, BERTScore and COMET-22 for German-English, and UniTE, UniTE-ref, XL-DA and xxl-DA19 for English-German.Metrics in both directions are performing worst when it comes to named-entities {\&} terminology and particularly measuring units. Particularly in German-English they are weak at detecting issues at punctuation, polar questions, relative clauses, dates and idioms. In English-German, they perform worst at present progressive of transitive verbs, future II progressive of intransitive verbs, simple present perfect of ditransitive verbs and focus particles."
}