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        <title>Incorporating Dialectal Variability for Socially Equitable Language Identification</title>
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        <namePart type="given">David</namePart>
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        <namePart type="given">Dan</namePart>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Regina</namePart>
            <namePart type="family">Barzilay</namePart>
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        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
            <namePart type="family">Kan</namePart>
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    <abstract>Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2009</identifier>
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%0 Conference Proceedings
%T Incorporating Dialectal Variability for Socially Equitable Language Identification
%A Jurgens, David
%A Tsvetkov, Yulia
%A Jurafsky, Dan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F jurgens-etal-2017-incorporating
%X Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.
%R 10.18653/v1/P17-2009
%U https://aclanthology.org/P17-2009
%U https://doi.org/10.18653/v1/P17-2009
%P 51-57

Markdown (Informal)

[Incorporating Dialectal Variability for Socially Equitable Language Identification](https://aclanthology.org/P17-2009) (Jurgens et al., ACL 2017)

• Incorporating Dialectal Variability for Socially Equitable Language Identification (Jurgens et al., ACL 2017)

ACL

• David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017. Incorporating Dialectal Variability for Socially Equitable Language Identification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 51–57, Vancouver, Canada. Association for Computational Linguistics.