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        <title>Boosting Unsupervised Machine Translation with Pseudo-Parallel Data</title>
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        <namePart type="given">Ivana</namePart>
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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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    <abstract>Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.</abstract>
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%0 Conference Proceedings
%T Boosting Unsupervised Machine Translation with Pseudo-Parallel Data
%A Kvapilíková, Ivana
%A Bojar, Ondřej
%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 kvapilikova-bojar-2023-boosting
%X Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.
%U https://aclanthology.org/2023.mtsummit-research.12
%P 135-147

Markdown (Informal)

[Boosting Unsupervised Machine Translation with Pseudo-Parallel Data](https://aclanthology.org/2023.mtsummit-research.12) (Kvapilíková & Bojar, MTSummit 2023)

• Boosting Unsupervised Machine Translation with Pseudo-Parallel Data (Kvapilíková & Bojar, MTSummit 2023)

ACL

• Ivana Kvapilíková and Ondřej Bojar. 2023. Boosting Unsupervised Machine Translation with Pseudo-Parallel Data. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 135–147, Macau SAR, China. Asia-Pacific Association for Machine Translation.