@inproceedings{zafeiridou-sofianopoulos-2022-welocalize,
    title = "Welocalize-{ARC}/{NKUA}`s Submission to the {WMT} 2022 Quality Estimation Shared Task",
    author = "Zafeiridou, Eirini  and
      Sofianopoulos, Sokratis",
    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-1.62/",
    pages = "653--660",
    abstract = "This paper presents our submission to the WMT 2022 quality estimation shared task and more specifically to the quality prediction sentence-level direct assessment (DA) subtask. We build a multilingual system based on the predictor{--}estimator architecture by using the XLM-RoBERTa transformer for feature extraction and a regression head on top of the final model to estimate the $z$-standardized DA labels. Furthermore, we use pretrained models to extract useful knowledge that reflect various criteria of quality assessment and demonstrate good correlation with human judgements. We optimize the performance of our model by incorporating this information as additional external features in the input data and by applying Monte Carlo dropout during both training and inference."
}