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        <title>LanideNN: Multilingual Language Identification on Character Window</title>
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    <name type="personal">
        <namePart type="given">Tom</namePart>
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            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers</title>
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        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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            <namePart type="given">Phil</namePart>
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            <namePart type="given">Alexander</namePart>
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    <abstract>In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. Monolingual language identification assumes that the given document is written in one language. In multilingual language identification, the document is usually in two or three languages and we just want their names. We aim one step further and propose a method for textual language identification where languages can change arbitrarily and the goal is to identify the spans of each of the languages. Our method is based on Bidirectional Recurrent Neural Networks and it performs well in monolingual and multilingual language identification tasks on six datasets covering 131 languages. The method keeps the accuracy also for short documents and across domains, so it is ideal for off-the-shelf use without preparation of training data.</abstract>
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%0 Conference Proceedings
%T LanideNN: Multilingual Language Identification on Character Window
%A Kocmi, Tom
%A Bojar, Ondřej
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F kocmi-bojar-2017-lanidenn
%X In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. Monolingual language identification assumes that the given document is written in one language. In multilingual language identification, the document is usually in two or three languages and we just want their names. We aim one step further and propose a method for textual language identification where languages can change arbitrarily and the goal is to identify the spans of each of the languages. Our method is based on Bidirectional Recurrent Neural Networks and it performs well in monolingual and multilingual language identification tasks on six datasets covering 131 languages. The method keeps the accuracy also for short documents and across domains, so it is ideal for off-the-shelf use without preparation of training data.
%U https://aclanthology.org/E17-1087
%P 927-936

Markdown (Informal)

[LanideNN: Multilingual Language Identification on Character Window](https://aclanthology.org/E17-1087) (Kocmi & Bojar, EACL 2017)

• LanideNN: Multilingual Language Identification on Character Window (Kocmi & Bojar, EACL 2017)

ACL

• Tom Kocmi and Ondřej Bojar. 2017. LanideNN: Multilingual Language Identification on Character Window. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 927–936, Valencia, Spain. Association for Computational Linguistics.