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        <title>Character alignment methods for dialect-to-standard normalization</title>
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            <title>Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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        <name type="personal">
            <namePart type="given">Eleanor</namePart>
            <namePart type="family">Chodroff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Frederic</namePart>
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    <abstract>This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions. We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets. In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task. We find that trained alignment methods only show marginal benefits to simple Levenshtein distance. On this particular task, eflomal outperforms related methods such as GIZA++ or fast_align by a large margin.</abstract>
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    <identifier type="doi">10.18653/v1/2023.sigmorphon-1.12</identifier>
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%0 Conference Proceedings
%T Character alignment methods for dialect-to-standard normalization
%A Scherrer, Yves
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F scherrer-2023-character
%X This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions. We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets. In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task. We find that trained alignment methods only show marginal benefits to simple Levenshtein distance. On this particular task, eflomal outperforms related methods such as GIZA++ or fast_align by a large margin.
%R 10.18653/v1/2023.sigmorphon-1.12
%U https://aclanthology.org/2023.sigmorphon-1.12
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.12
%P 110-116

Markdown (Informal)

[Character alignment methods for dialect-to-standard normalization](https://aclanthology.org/2023.sigmorphon-1.12) (Scherrer, SIGMORPHON 2023)

• Character alignment methods for dialect-to-standard normalization (Scherrer, SIGMORPHON 2023)

ACL

• Yves Scherrer. 2023. Character alignment methods for dialect-to-standard normalization. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 110–116, Toronto, Canada. Association for Computational Linguistics.