@inproceedings{picinini-2022-improving,
    title = "Improving Consistency of Human and Machine Translations",
    author = "Picinini, Silvio",
    editor = "Campbell, Janice  and
      Larocca, Stephen  and
      Marciano, Jay  and
      Savenkov, Konstantin  and
      Yanishevsky, Alex",
    booktitle = "Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)",
    month = sep,
    year = "2022",
    address = "Orlando, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.amta-upg.8/",
    pages = "107--122",
    abstract = "Consistency is one of the desired quality features in final translations. For human-only translations (without MT), we rely on the translator{'}s ability to achieve consistency. For MT, consistency is neither guaranteed nor expected. MT may actually generate inconsistencies, and it is left to the post-editor to introduce consistency in a manual fashion. This work presents a method that facilitates the improvement of consistency without the need of a glossary. It detects inconsistencies in the post-edited work, and gives the post-editor the opportunity to fix the translation towards consistency. We describe the method, which is simple and involves only a short Python script, and also provide numbers that show its positive impact. This method is a contribution to a broader set of quality checks that can improve language quality of human and MT translations."
}