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    <titleInfo>
        <title>Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages</title>
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    <name type="personal">
        <namePart type="given">Manuel</namePart>
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    <name type="personal">
        <namePart type="given">Elisabeth</namePart>
        <namePart type="family">Mager</namePart>
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    <name type="personal">
        <namePart type="given">Alfonso</namePart>
        <namePart type="family">Medina-Urrea</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="given">Vladimir</namePart>
        <namePart type="family">Meza Ruiz</namePart>
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    <name type="personal">
        <namePart type="given">Katharina</namePart>
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            <title>Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages</title>
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            <namePart type="given">Judith</namePart>
            <namePart type="given">L</namePart>
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    <abstract>Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of the art for high-resource and more intensively studied language pairs. To shed light on the phenomena which hamper automatic translation to and from polysynthetic languages, we study translations from three low-resource, polysynthetic languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa. Doing so, we find that in a morpheme-to-morpheme alignment an important amount of information contained in polysynthetic morphemes has no Spanish counterpart, and its translation is often omitted. We further conduct a qualitative analysis and, thus, identify morpheme types that are commonly hard to align or ignored in the translation process.</abstract>
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%0 Conference Proceedings
%T Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
%A Mager, Manuel
%A Mager, Elisabeth
%A Medina-Urrea, Alfonso
%A Meza Ruiz, Ivan Vladimir
%A Kann, Katharina
%Y Klavans, Judith L.
%S Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F mager-etal-2018-lost
%X Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of the art for high-resource and more intensively studied language pairs. To shed light on the phenomena which hamper automatic translation to and from polysynthetic languages, we study translations from three low-resource, polysynthetic languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa. Doing so, we find that in a morpheme-to-morpheme alignment an important amount of information contained in polysynthetic morphemes has no Spanish counterpart, and its translation is often omitted. We further conduct a qualitative analysis and, thus, identify morpheme types that are commonly hard to align or ignored in the translation process.
%U https://aclanthology.org/W18-4808
%P 73-83

Markdown (Informal)

[Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages](https://aclanthology.org/W18-4808) (Mager et al., PYLO 2018)

• Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages (Mager et al., PYLO 2018)

ACL

• Manuel Mager, Elisabeth Mager, Alfonso Medina-Urrea, Ivan Vladimir Meza Ruiz, and Katharina Kann. 2018. Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages. In Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages, pages 73–83, Santa Fe, New Mexico, USA. Association for Computational Linguistics.