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    <titleInfo>
        <title>Considerations for meaningful sign language machine translation based on glosses</title>
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    <name type="personal">
        <namePart type="given">Mathias</namePart>
        <namePart type="family">Müller</namePart>
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    <name type="personal">
        <namePart type="given">Zifan</namePart>
        <namePart type="family">Jiang</namePart>
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    <name type="personal">
        <namePart type="given">Amit</namePart>
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    <name type="personal">
        <namePart type="given">Annette</namePart>
        <namePart type="family">Rios</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="family">Ebling</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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        <name type="personal">
            <namePart type="given">Jordan</namePart>
            <namePart type="family">Boyd-Graber</namePart>
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        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
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    <abstract>Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-short.60</identifier>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Considerations for meaningful sign language machine translation based on glosses
%A Müller, Mathias
%A Jiang, Zifan
%A Moryossef, Amit
%A Rios, Annette
%A Ebling, Sarah
%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 muller-etal-2023-considerations
%X Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.
%R 10.18653/v1/2023.acl-short.60
%U https://aclanthology.org/2023.acl-short.60
%U https://doi.org/10.18653/v1/2023.acl-short.60
%P 682-693

Markdown (Informal)

[Considerations for meaningful sign language machine translation based on glosses](https://aclanthology.org/2023.acl-short.60) (Müller et al., ACL 2023)

• Considerations for meaningful sign language machine translation based on glosses (Müller et al., ACL 2023)

ACL

• Mathias Müller, Zifan Jiang, Amit Moryossef, Annette Rios, and Sarah Ebling. 2023. Considerations for meaningful sign language machine translation based on glosses. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 682–693, Toronto, Canada. Association for Computational Linguistics.