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        <title>A Holistic Approach to Reference-Free Evaluation of Machine Translation</title>
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        <namePart type="given">Hanming</namePart>
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        <namePart type="given">Wenjuan</namePart>
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    <name type="personal">
        <namePart type="given">Jinan</namePart>
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    <abstract>Traditional machine translation evaluation relies on reference written by humans. While reference-free evaluation gets rid of the constraints of labor-intensive annotations, which can pivot easily to new domains and is more scalable. In this paper, we propose a reference-free evaluation approach that characterizes evaluation as two aspects: (1) fluency: how well the translated text conforms to normal human language usage; (2) faithfulness: how well the translated text reflects the source data. We further split the faithfulness into word-level and sentence-level. Extensive experiments spanning WMT18/19/21 Metrics segment-level daRR and MQM datasets demonstrate that our proposed reference-free approach, ReFreeEval, outperforms SOTA reference-fee metrics like YiSi-2.</abstract>
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%0 Conference Proceedings
%T A Holistic Approach to Reference-Free Evaluation of Machine Translation
%A Wu, Hanming
%A Han, Wenjuan
%A Di, Hui
%A Chen, Yufeng
%A Xu, Jinan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-holistic
%X Traditional machine translation evaluation relies on reference written by humans. While reference-free evaluation gets rid of the constraints of labor-intensive annotations, which can pivot easily to new domains and is more scalable. In this paper, we propose a reference-free evaluation approach that characterizes evaluation as two aspects: (1) fluency: how well the translated text conforms to normal human language usage; (2) faithfulness: how well the translated text reflects the source data. We further split the faithfulness into word-level and sentence-level. Extensive experiments spanning WMT18/19/21 Metrics segment-level daRR and MQM datasets demonstrate that our proposed reference-free approach, ReFreeEval, outperforms SOTA reference-fee metrics like YiSi-2.
%R 10.18653/v1/2023.acl-short.55
%U https://aclanthology.org/2023.acl-short.55
%U https://doi.org/10.18653/v1/2023.acl-short.55
%P 623-636

Markdown (Informal)

[A Holistic Approach to Reference-Free Evaluation of Machine Translation](https://aclanthology.org/2023.acl-short.55) (Wu et al., ACL 2023)

• A Holistic Approach to Reference-Free Evaluation of Machine Translation (Wu et al., ACL 2023)

ACL

• Hanming Wu, Wenjuan Han, Hui Di, Yufeng Chen, and Jinan Xu. 2023. A Holistic Approach to Reference-Free Evaluation of Machine Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 623–636, Toronto, Canada. Association for Computational Linguistics.