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    <titleInfo>
        <title>Neural Machine Translation for Sinhala-English Code-Mixed Text</title>
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        <namePart type="given">Archchana</namePart>
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        <dateIssued>2021-sep</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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    <abstract>Code-mixing has become a moving method of communication among multilingual speakers. Most of the social media content of the multilingual societies are written in code-mixed text. However, most of the current translation systems neglect to convert code-mixed texts to a standard language. Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus. This paper proposes a Neural Machine Translation(NMT) model to translate the Sinhala-English code-mixed text to the Sinhala language. Due to the limited resources available for Sinhala-English code-mixed(SECM) text, a parallel corpus is created with SECM sentences and Sinhala sentences. Srilankan social media sites contain SECM texts more frequently than the standard languages. The model proposed for code-mixed text translation in this study is a combination of Encoder-Decoder framework with LSTM units and Teachers Forcing Algorithm. The translated sentences from the model are evaluated using BLEU(Bilingual Evaluation Understudy) metric. Our model achieved a remarkable BLEU score for the translation.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.82</url>
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        <date>2021-sep</date>
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            <start>718</start>
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%0 Conference Proceedings
%T Neural Machine Translation for Sinhala-English Code-Mixed Text
%A Kugathasan, Archchana
%A Sumathipala, Sagara
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 sep
%I INCOMA Ltd.
%C Held Online
%F kugathasan-sumathipala-2021-neural
%X Code-mixing has become a moving method of communication among multilingual speakers. Most of the social media content of the multilingual societies are written in code-mixed text. However, most of the current translation systems neglect to convert code-mixed texts to a standard language. Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus. This paper proposes a Neural Machine Translation(NMT) model to translate the Sinhala-English code-mixed text to the Sinhala language. Due to the limited resources available for Sinhala-English code-mixed(SECM) text, a parallel corpus is created with SECM sentences and Sinhala sentences. Srilankan social media sites contain SECM texts more frequently than the standard languages. The model proposed for code-mixed text translation in this study is a combination of Encoder-Decoder framework with LSTM units and Teachers Forcing Algorithm. The translated sentences from the model are evaluated using BLEU(Bilingual Evaluation Understudy) metric. Our model achieved a remarkable BLEU score for the translation.
%U https://aclanthology.org/2021.ranlp-1.82
%P 718-726

Markdown (Informal)

[Neural Machine Translation for Sinhala-English Code-Mixed Text](https://aclanthology.org/2021.ranlp-1.82) (Kugathasan & Sumathipala, RANLP 2021)

• Neural Machine Translation for Sinhala-English Code-Mixed Text (Kugathasan & Sumathipala, RANLP 2021)

ACL

• Archchana Kugathasan and Sagara Sumathipala. 2021. Neural Machine Translation for Sinhala-English Code-Mixed Text. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 718–726, Held Online. INCOMA Ltd..