@inproceedings{bittlingmayer-etal-2022-quality,
    title = "Quality Prediction",
    author = "Bittlingmayer, Adam  and
      Zubarev, Boris  and
      Aleksanyan, Artur",
    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/jlcl-multiple-ingestion/2022.amta-upg.12/",
    pages = "159--180",
    abstract = "A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We`ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI."
}