@inproceedings{tryhubyshyn-etal-2023-bad,
    title = "Bad {MT} Systems are Good for Quality Estimation",
    author = "Tryhubyshyn, Iryna  and
      Tamchyna, Ale{\v{s}}  and
      Bojar, Ond{\v{r}}ej",
    editor = "Utiyama, Masao  and
      Wang, Rui",
    booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track",
    month = sep,
    year = "2023",
    address = "Macau SAR, China",
    publisher = "Asia-Pacific Association for Machine Translation",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.mtsummit-research.17/",
    pages = "200--208",
    abstract = "Quality estimation (QE) is the task of predicting quality of outputs produced by machine translation (MT) systems. Currently, the highest-performing QE systems are supervised and require training on data with golden quality scores. In this paper, we investigate the impact of the quality of the underlying MT outputs on the performance of QE systems. We find that QE models trained on datasets with lower-quality translations often outperform those trained on higher-quality data. We also demonstrate that good performance can be achieved by using a mix of data from different MT systems."
}