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Register a new userIIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for Indian languages
IIIt Hyderabad التقديم إلى WAT 2021: فعالة أنظمة NMT متعددة اللغات متعددة اللغات الهندية
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تصف هذه الورقة العمل والأنظمة المقدمة من فريق IIIT-HYDERBAD في مهمة WAT 2021 Multiindicmt المشتركة. تغطي المهمة 10 لغات رئيسية من شبه القارة الهندية. بالنسبة لنطاق هذه المهمة، قمنا ببناء أنظمة متعددة اللغات لمدة 20 ساعة توسيعية وهي الإنجليزية-MED (ONE-LICONE) وإرش إنجليزي (كثير إلى واحد). منفردة، اللغات الهندية هي فقراء الموارد التي تعيق جودة الترجمة ولكن من خلال الاستفادة من تعدد اللغات والهدوضة غير اللغوية والنباتية، يمكن تعزيز جودة الترجمة بشكل كبير. لكن أنظمة متعددة اللغات معقدة للغاية من حيث الوقت وكذلك الموارد الحسابية. لذلك، نحن ندرب أنظمتنا من خلال إلقاء البيانات الكفاءة التي سيساهم في الواقع في معظم عملية التعلم. علاوة على ذلك، نحن نستغل أيضا اللغة المتعلقة بعثر بين اللغات الهندية. تم إجراء جميع المقارنات باستخدام نقاط بلو ووجدت أن نظامنا متعدد اللغات النهائي يتفوق بشكل كبير على خطوط الأساس بمعدل 11.3 و 19.6 نقاط بلو لترويج الإنجليزي (EN-XX) وإرادي الإنشاءات الإنجليزي (XX-EN) ، على التوالى.
This paper describes the work and the systems submitted by the IIIT-Hyderbad team in the WAT 2021 MultiIndicMT shared task. The task covers 10 major languages of the Indian subcontinent. For the scope of this task, we have built multilingual systems for 20 translation directions namely English-Indic (one-to- many) and Indic-English (many-to-one). Individually, Indian languages are resource poor which hampers translation quality but by leveraging multilingualism and abundant monolingual corpora, the translation quality can be substantially boosted. But the multilingual systems are highly complex in terms of time as well as computational resources. Therefore, we are training our systems by efficiently se- lecting data that will actually contribute to most of the learning process. Furthermore, we are also exploiting the language related- ness found in between Indian languages. All the comparisons were made using BLEU score and we found that our final multilingual sys- tem significantly outperforms the baselines by an average of 11.3 and 19.6 BLEU points for English-Indic (en-xx) and Indic-English (xx- en) directions, respectively.
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