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        <title>A reproduction of Apple’s bi-directional LSTM models for language identification in short strings</title>
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            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop</title>
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            <namePart type="given">Ionut-Teodor</namePart>
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            <namePart type="given">Madhumita</namePart>
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    <abstract>Language Identification is the task of identifying a document’s language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM model’s performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-srw.6</identifier>
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%0 Conference Proceedings
%T A reproduction of Apple’s bi-directional LSTM models for language identification in short strings
%A Toftrup, Mads
%A Asger Sørensen, Søren
%A Ciosici, Manuel R.
%A Assent, Ira
%Y Sorodoc, Ionut-Teodor
%Y Sushil, Madhumita
%Y Takmaz, Ece
%Y Agirre, Eneko
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F toftrup-etal-2021-reproduction
%X Language Identification is the task of identifying a document’s language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM model’s performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.
%R 10.18653/v1/2021.eacl-srw.6
%U https://aclanthology.org/2021.eacl-srw.6
%U https://doi.org/10.18653/v1/2021.eacl-srw.6
%P 36-42

Markdown (Informal)

[A reproduction of Apple’s bi-directional LSTM models for language identification in short strings](https://aclanthology.org/2021.eacl-srw.6) (Toftrup et al., EACL 2021)

• A reproduction of Apple’s bi-directional LSTM models for language identification in short strings (Toftrup et al., EACL 2021)

ACL

• Mads Toftrup, Søren Asger Sørensen, Manuel R. Ciosici, and Ira Assent. 2021. A reproduction of Apple’s bi-directional LSTM models for language identification in short strings. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 36–42, Online. Association for Computational Linguistics.