@inproceedings{polak-etal-2021-explainable,
    title = "Explainable Quality Estimation: {CUNI} {E}val4{NLP} Submission",
    author = "Pol{\'a}k, Peter  and
      Singh, Muskaan  and
      Bojar, Ond{\v{r}}ej",
    editor = "Gao, Yang  and
      Eger, Steffen  and
      Zhao, Wei  and
      Lertvittayakumjorn, Piyawat  and
      Fomicheva, Marina",
    booktitle = "Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.eval4nlp-1.24/",
    doi = "10.18653/v1/2021.eval4nlp-1.24",
    pages = "250--255",
    abstract = "This paper describes our participating system in the shared task Explainable quality estimation of 2nd Workshop on Evaluation {\&} Comparison of NLP Systems. The task of quality estimation (QE, a.k.a. reference-free evaluation) is to predict the quality of MT output at inference time without access to reference translations. In this proposed work, we first build a word-level quality estimation model, then we finetune this model for sentence-level QE. Our proposed models achieve near state-of-the-art results. In the word-level QE, we place 2nd and 3rd on the supervised Ro-En and Et-En test sets. In the sentence-level QE, we achieve a relative improvement of 8.86{\%} (Ro-En) and 10.6{\%} (Et-En) in terms of the Pearson correlation coefficient over the baseline model."
}