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            <title>Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track</title>
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            <namePart type="given">Masao</namePart>
            <namePart type="family">Utiyama</namePart>
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            <namePart type="given">Rui</namePart>
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            <publisher>Asia-Pacific Association for Machine Translation</publisher>
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    <abstract>We investigate whether using semi-supervised learning (SSL) methods can be beneficial for the task of word-level Quality Estimation of Machine Translation in low resource conditions. We show that the Mean Teacher network can provide equal or significantly better MCC scores (up to +12%) than supervised methods when a limited amount of labeled data is available. Additionally, following previous work on SSL, we investigate Pseudo-Labeling in combination with SSL, which nevertheless does not provide consistent improvements.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Learning for Quality Estimation of Machine Translation
%A Bhatia, Tarun
%A Kraemer, Martin
%A Vellasques, Eduardo
%A Avramidis, Eleftherios
%Y Utiyama, Masao
%Y Wang, Rui
%S Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F bhatia-etal-2023-semi
%X We investigate whether using semi-supervised learning (SSL) methods can be beneficial for the task of word-level Quality Estimation of Machine Translation in low resource conditions. We show that the Mean Teacher network can provide equal or significantly better MCC scores (up to +12%) than supervised methods when a limited amount of labeled data is available. Additionally, following previous work on SSL, we investigate Pseudo-Labeling in combination with SSL, which nevertheless does not provide consistent improvements.
%U https://aclanthology.org/2023.mtsummit-research.7
%P 72-83

Markdown (Informal)

[Semi-supervised Learning for Quality Estimation of Machine Translation](https://aclanthology.org/2023.mtsummit-research.7) (Bhatia et al., MTSummit 2023)

• Semi-supervised Learning for Quality Estimation of Machine Translation (Bhatia et al., MTSummit 2023)

ACL

• Tarun Bhatia, Martin Kraemer, Eduardo Vellasques, and Eleftherios Avramidis. 2023. Semi-supervised Learning for Quality Estimation of Machine Translation. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 72–83, Macau SAR, China. Asia-Pacific Association for Machine Translation.