Explainable Quality Estimation: CUNI Eval4NLP Submission
Peter Polák, Muskaan Singh, Ondřej Bojar · 2021
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 stateof-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.