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        <title>The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation</title>
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            <namePart type="given">Ionut-Teodor</namePart>
            <namePart type="family">Sorodoc</namePart>
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        <name type="personal">
            <namePart type="given">Madhumita</namePart>
            <namePart type="family">Sushil</namePart>
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            <namePart type="given">Ece</namePart>
            <namePart type="family">Takmaz</namePart>
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    <abstract>This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.</abstract>
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%0 Conference Proceedings
%T The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation
%A Saleva, Jonne
%A Lignos, Constantine
%Y Sorodoc, Ionut-Teodor
%Y Sushil, Madhumita
%Y Takmaz, Ece
%Y Agirre, Eneko
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F saleva-lignos-2021-effectiveness
%X This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.
%R 10.18653/v1/2021.eacl-srw.22
%U https://aclanthology.org/2021.eacl-srw.22
%U https://doi.org/10.18653/v1/2021.eacl-srw.22
%P 164-174

Markdown (Informal)

[The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation](https://aclanthology.org/2021.eacl-srw.22) (Saleva & Lignos, EACL 2021)

• The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation (Saleva & Lignos, EACL 2021)

ACL

• Jonne Saleva and Constantine Lignos. 2021. The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 164–174, Online. Association for Computational Linguistics.