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Register a new userDomain Adaptation for Hindi-Telugu Machine Translation Using Domain Specific Back Translation
تكيف مجال الترجمة الآلية الهندية التيلجو باستخدام ترجمة المجال الخاصة بالترجمة
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في هذه الورقة، نقدم نهجا جديدا لتكييف المجال في الجهاز العصبي الذي يهدف إلى تحسين جودة Thetranslation على نطاق جديد. إضافة مجالات جديدة هي مهمة عالية تحديا لبيانات الترجمة الآلية العصبية، يصبح أكثر عبادة منتشرةالمجالات الفنية مثل Chem-Istry والذكاء الاصطناعي بسبب مصطلحات Spe-Sicific، إلخ. نقترح أسلوب الترجمة الخلفي العجول Domainspecific والتي تنوع بيانات الأحادية المتوفرة والبيانات الاصطناعية العامة بطريقة مختلفة. هذا النهج يستخدم خارج الكلمات. النهجعام جدا ويمكن أن تقوم بالياف بأي زوج لغة لأي مجال.نقوم بإجراء تجاربنا على الكنديمان والذكاء الاصطناعي) من أجل اللغة الهندية والتيلجو في كل من direc-tions.وقد لوحظ أن استخدام البيانات الاصطناعية الاستخدام التي تم إنشاؤها بواسطة proposedalgorithm يحسن درجات بلو بشكل كبير.
In this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain.Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited data, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial Intelligence due to spe-cific terminology, etc. We propose DomainSpecific Back Translation method whichuses available monolingual data and gen-erates synthetic data in a different way.This approach uses Out Of Domain words.The approach is very generic and can beapplied to any language pair for any domain. We conduct our experiments onChemistry and Artificial Intelligence do-mains for Hindi and Telugu in both direc-tions. It has been observed that the usageof synthetic data created by the proposedalgorithm improves the BLEU scores significantly.
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