<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kuparinen-etal-2023-dialect">
    <titleInfo>
        <title>Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Olli</namePart>
        <namePart type="family">Kuparinen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aleksandra</namePart>
        <namePart type="family">Miletić</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yves</namePart>
        <namePart type="family">Scherrer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Text normalization methods have been commonly applied to historical language or user-generated content, but less often to dialectal transcriptions. In this paper, we introduce dialect-to-standard normalization – i.e., mapping phonetic transcriptions from different dialects to the orthographic norm of the standard variety – as a distinct sentence-level character transduction task and provide a large-scale analysis of dialect-to-standard normalization methods. To this end, we compile a multilingual dataset covering four languages: Finnish, Norwegian, Swiss German and Slovene. For the two biggest corpora, we provide three different data splits corresponding to different use cases for automatic normalization. We evaluate the most successful sequence-to-sequence model architectures proposed for text normalization tasks using different tokenization approaches and context sizes. We find that a character-level Transformer trained on sliding windows of three words works best for Finnish, Swiss German and Slovene, whereas the pre-trained byT5 model using full sentences obtains the best results for Norwegian. Finally, we perform an error analysis to evaluate the effect of different data splits on model performance.</abstract>
    <identifier type="citekey">kuparinen-etal-2023-dialect</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.923</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.923/</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>13814</start>
            <end>13828</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation
%A Kuparinen, Olli
%A Miletić, Aleksandra
%A Scherrer, Yves
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kuparinen-etal-2023-dialect
%X Text normalization methods have been commonly applied to historical language or user-generated content, but less often to dialectal transcriptions. In this paper, we introduce dialect-to-standard normalization – i.e., mapping phonetic transcriptions from different dialects to the orthographic norm of the standard variety – as a distinct sentence-level character transduction task and provide a large-scale analysis of dialect-to-standard normalization methods. To this end, we compile a multilingual dataset covering four languages: Finnish, Norwegian, Swiss German and Slovene. For the two biggest corpora, we provide three different data splits corresponding to different use cases for automatic normalization. We evaluate the most successful sequence-to-sequence model architectures proposed for text normalization tasks using different tokenization approaches and context sizes. We find that a character-level Transformer trained on sliding windows of three words works best for Finnish, Swiss German and Slovene, whereas the pre-trained byT5 model using full sentences obtains the best results for Norwegian. Finally, we perform an error analysis to evaluate the effect of different data splits on model performance.
%R 10.18653/v1/2023.findings-emnlp.923
%U https://aclanthology.org/2023.findings-emnlp.923/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.923
%P 13814-13828

Markdown (Informal)

[Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation](https://aclanthology.org/2023.findings-emnlp.923/) (Kuparinen et al., Findings 2023)

• Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation (Kuparinen et al., Findings 2023)

ACL

• Olli Kuparinen, Aleksandra Miletić, and Yves Scherrer. 2023. Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13814–13828, Singapore. Association for Computational Linguistics.