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        <namePart type="given">Muskaan</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>This paper describes our participating system in the shared task Explainable quality estimation of 2nd Workshop on Evaluation &amp; 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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eval4nlp-1.24</identifier>
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%0 Conference Proceedings
%T Explainable Quality Estimation: CUNI Eval4NLP Submission
%A Polák, Peter
%A Singh, Muskaan
%A Bojar, Ondřej
%Y Gao, Yang
%Y Eger, Steffen
%Y Zhao, Wei
%Y Lertvittayakumjorn, Piyawat
%Y Fomicheva, Marina
%S Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F polak-etal-2021-explainable
%X 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.
%R 10.18653/v1/2021.eval4nlp-1.24
%U https://aclanthology.org/2021.eval4nlp-1.24/
%U https://doi.org/10.18653/v1/2021.eval4nlp-1.24
%P 250-255

Markdown (Informal)

[Explainable Quality Estimation: CUNI Eval4NLP Submission](https://aclanthology.org/2021.eval4nlp-1.24/) (Polák et al., Eval4NLP 2021)

• Explainable Quality Estimation: CUNI Eval4NLP Submission (Polák et al., Eval4NLP 2021)

ACL

• Peter Polák, Muskaan Singh, and Ondřej Bojar. 2021. Explainable Quality Estimation: CUNI Eval4NLP Submission. In Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, pages 250–255, Punta Cana, Dominican Republic. Association for Computational Linguistics.