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        <title>Joint Part-of-Speech and Language ID Tagging for Code-Switched Data</title>
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            <title>Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching</title>
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            <namePart type="given">Gustavo</namePart>
            <namePart type="family">Aguilar</namePart>
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        <name type="personal">
            <namePart type="given">Fahad</namePart>
            <namePart type="family">AlGhamdi</namePart>
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    <abstract>Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34% accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.</abstract>
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%0 Conference Proceedings
%T Joint Part-of-Speech and Language ID Tagging for Code-Switched Data
%A Soto, Victor
%A Hirschberg, Julia
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F soto-hirschberg-2018-joint
%X Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34% accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.
%R 10.18653/v1/W18-3201
%U https://aclanthology.org/W18-3201
%U https://doi.org/10.18653/v1/W18-3201
%P 1-10

Markdown (Informal)

[Joint Part-of-Speech and Language ID Tagging for Code-Switched Data](https://aclanthology.org/W18-3201) (Soto & Hirschberg, ACL 2018)

• Joint Part-of-Speech and Language ID Tagging for Code-Switched Data (Soto & Hirschberg, ACL 2018)

ACL

• Victor Soto and Julia Hirschberg. 2018. Joint Part-of-Speech and Language ID Tagging for Code-Switched Data. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 1–10, Melbourne, Australia. Association for Computational Linguistics.