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Register a new userTowards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation
نحو تطوير سؤال مرئي متعدد اللغات ومزوج التعليمات البرمجية من خلال تنطير المعرفة
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أظهرت نماذج الرؤية اللغوية المدربة مسبقا أداء رائعا حول مهمة الإجابة على السؤال المرئي (VQA). ومع ذلك، يتم تدريب معظم النماذج المدربة مسبقا من خلال النظر فقط في التعلم أحادي الأونلينغ، وخاصة اللغة الغنية بالموارد مثل اللغة الإنجليزية. تدريب هذه النماذج للكمات متعددة اللغات طلب موارد الحوسبة عالية ومجموعات بيانات الرؤية متعددة اللغات التي تعيق تطبيقها في الممارسة العملية. لتخفيف هذه التحديات، نقترح نهج تقطير المعرفة لتوسيع نموذج للرؤية باللغة الإنجليزية (المعلم) في نموذج متعدد اللغات ومزوج التعليمات البرمجية (طالبة). على عكس أساليب تقطير المعرفة الحالية، والتي تستخدم فقط الإخراج من الطبقة الأخيرة من شبكة المعلم للتقطير، يتعلم نموذج الطالب الخاص بنا وتقليد المعلم من طبقات متعددة الوسائط (تشفير اللغة والرؤية) بأهداف تقطير مصممة بشكل مناسب لاستخراج المعرفة الإضافية وبعد كما نقوم بإنشاء مجموعة بيانات VQA متعددة اللغات متعددة اللغات متعددة اللغات وخلطها في أحد عشر جهازا مختلفا للنظر في اللغات الهندية والأوروبية المتعددة. تظهر النتائج التجريبية والتحليل المتعمق فعالية نموذج VQA المقترح على نماذج الرؤية المدربة مسبقا في الرؤية المدربة مسبقا في أحد عشر من إعدادات لغة متنوعة.
Pre-trained language-vision models have shown remarkable performance on the visual question answering (VQA) task. However, most pre-trained models are trained by only considering monolingual learning, especially the resource-rich language like English. Training such models for multilingual setups demand high computing resources and multilingual language-vision dataset which hinders their application in practice. To alleviate these challenges, we propose a knowledge distillation approach to extend an English language-vision model (teacher) into an equally effective multilingual and code-mixed model (student). Unlike the existing knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model learns and imitates the teacher from multiple intermediate layers (language and vision encoders) with appropriately designed distillation objectives for incremental knowledge extraction. We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages. Experimental results and in-depth analysis show the effectiveness of the proposed VQA model over the pre-trained language-vision models on eleven diverse language setups.
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