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Register a new userNeural Machine Translation for Sinhala-English Code-Mixed Text
الترجمة الآلية العصبية لنص Sinhala-English
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أصبح خلط التعليمات البرمجية طريقة متحركة للاتصال بين مكبرات الصوت متعددة اللغات. تتم كتابة معظم محتوى وسائل التواصل الاجتماعي للمجتمعات متعددة اللغات في النص المختلط من التعليمات البرمجية. ومع ذلك، فإن معظم أنظمة الترجمة الحالية إهمال تحويل النصوص المختلطة من التعليمات البرمجية إلى لغة قياسية. تظل معظم المحتوى المكتوب من المستخدمين من المستخدمين في وسائل الإعلام الاجتماعية غير المعتمدة بسبب عدم توفر الموارد اللغوية مثل Corpus الموازي. تقترح هذه الورقة نموذجا للترجمة الآلية العصبية (NMT) لترجمة النص المختلط بين السنهالية - الإنجليزية إلى لغة سنهالا. نظرا للموارد المحدودة المتاحة لنص Sinhala-English النص المختلط (SEMM)، يتم إنشاء Corpus الموازي مع جمل SEMM وجمل Sinhala. تحتوي مواقع وسائل التواصل الاجتماعي Srilankan على نصوص SEMM بشكل متكرر أكثر من اللغات القياسية. النموذج المقترح للترجمة النصية المختلطة في التعليمات البرمجية في هذه الدراسة هو مزيج من إطار فك تشفير التشفير مع وحدات LSTM والمعلمين تجبر الخوارزمية. يتم تقييم الجمل المترجمة من النموذج باستخدام متري بلو (تقييم ثنائي اللغة). حقق نموذجنا درجة بلو رائعة للترجمة.
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.
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