@inproceedings{lim-park-2022-papagos,
    title = "Papago`s Submission to the {WMT}22 Quality Estimation Shared Task",
    author = "Lim, Seunghyun  and
      Park, Jeonghyeok",
    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-1.59/",
    pages = "627--633",
    abstract = "This paper describes anonymous submission to the WMT 2022 Quality Estimation shared task. We participate in Task 1: Quality Prediction for both sentence and word-level quality prediction tasks. Our system is a multilingual and multi-task model, whereby a single system can infer both sentence and word-level quality on multiple language pairs. Our system`s architecture consists of Pretrained Language Model (PLM) and task layers, and is jointly optimized for both sentence and word-level quality prediction tasks using multilingual dataset. We propose novel auxiliary tasks for training and explore diverse sources of additional data to demonstrate further improvements on performance. Through ablation study, we examine the effectiveness of proposed components and find optimal configurations to train our submission systems under each language pair and task settings. Finally, submission systems are trained and inferenced using K-folds ensemble. Our systems greatly outperform task organizer`s baseline and achieve comparable performance against other participants' submissions in both sentence and word-level quality prediction tasks."
}