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        <title>Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks</title>
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        <namePart type="given">Şaziye</namePart>
        <namePart type="given">Betül</namePart>
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            <namePart type="given">Marie-Catherine</namePart>
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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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    <abstract>Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-naacl.87</identifier>
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%0 Conference Proceedings
%T Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
%A Özateş, Şaziye Betül
%A Özgür, Arzucan
%A Gungor, Tunga
%A Çetinoğlu, Özlem
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F ozates-etal-2022-improving
%X Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.
%R 10.18653/v1/2022.findings-naacl.87
%U https://aclanthology.org/2022.findings-naacl.87
%U https://doi.org/10.18653/v1/2022.findings-naacl.87
%P 1159-1171

Markdown (Informal)

[Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks](https://aclanthology.org/2022.findings-naacl.87) (Özateş et al., Findings 2022)

• Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (Özateş et al., Findings 2022)

ACL

• Şaziye Betül Özateş, Arzucan Özgür, Tunga Gungor, and Özlem Çetinoğlu. 2022. Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1159–1171, Seattle, United States. Association for Computational Linguistics.