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        <title>COMET: A Neural Framework for MT Evaluation</title>
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        <namePart type="given">Ricardo</namePart>
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        <namePart type="given">Craig</namePart>
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        <namePart type="family">Farinha</namePart>
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    <name type="personal">
        <namePart type="given">Alon</namePart>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metric. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.213</identifier>
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%0 Conference Proceedings
%T COMET: A Neural Framework for MT Evaluation
%A Rei, Ricardo
%A Stewart, Craig
%A Farinha, Ana C.
%A Lavie, Alon
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F rei-etal-2020-comet
%X We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metric. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.
%R 10.18653/v1/2020.emnlp-main.213
%U https://aclanthology.org/2020.emnlp-main.213/
%U https://doi.org/10.18653/v1/2020.emnlp-main.213
%P 2685-2702

Markdown (Informal)

[COMET: A Neural Framework for MT Evaluation](https://aclanthology.org/2020.emnlp-main.213/) (Rei et al., EMNLP 2020)

• COMET: A Neural Framework for MT Evaluation (Rei et al., EMNLP 2020)

ACL

• Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020. COMET: A Neural Framework for MT Evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2685–2702, Online. Association for Computational Linguistics.