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        <title>Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data</title>
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        <namePart type="given">Aparna</namePart>
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            <title>Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference</title>
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        <name type="personal">
            <namePart type="given">Jonne</namePart>
            <namePart type="family">Sälevä</namePart>
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        <name type="personal">
            <namePart type="given">Constantine</namePart>
            <namePart type="family">Lignos</namePart>
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    <abstract>This paper reports work on building a word-level language identification (LID) model for code-mixed Bangla-English social media data using subword embeddings, with an ultimate goal of using this LID module as the first step in a modular part-of-speech (POS) tagger in future research. This work reports preliminary results of a word-level LID model that uses a single bidirectional LSTM with subword embeddings trained on very limited code-mixed resources. At the time of writing, there are no previous reported results available in which subword embeddings are used for language identification with the Bangla-English code-mixed language pair. As part of the current work, a labeled resource for word-level language identification is also presented, by correcting 85.7% of labels from the 2016 ICON Whatsapp Bangla-English dataset. The trained model was evaluated on a test set of 4,015 tokens compiled from the 2015 and 2016 ICON datasets, and achieved a test accuracy of 93.61%.</abstract>
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%0 Conference Proceedings
%T Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data
%A Dutta, Aparna
%Y Sälevä, Jonne
%Y Lignos, Constantine
%S Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F dutta-2022-word
%X This paper reports work on building a word-level language identification (LID) model for code-mixed Bangla-English social media data using subword embeddings, with an ultimate goal of using this LID module as the first step in a modular part-of-speech (POS) tagger in future research. This work reports preliminary results of a word-level LID model that uses a single bidirectional LSTM with subword embeddings trained on very limited code-mixed resources. At the time of writing, there are no previous reported results available in which subword embeddings are used for language identification with the Bangla-English code-mixed language pair. As part of the current work, a labeled resource for word-level language identification is also presented, by correcting 85.7% of labels from the 2016 ICON Whatsapp Bangla-English dataset. The trained model was evaluated on a test set of 4,015 tokens compiled from the 2015 and 2016 ICON datasets, and achieved a test accuracy of 93.61%.
%U https://aclanthology.org/2022.dclrl-1.10
%P 76-82

Markdown (Informal)

[Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data](https://aclanthology.org/2022.dclrl-1.10) (Dutta, DCLRL 2022)

• Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data (Dutta, DCLRL 2022)

ACL

• Aparna Dutta. 2022. Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 76–82, Marseille, France. European Language Resources Association.