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Register a new userImproved English to Hindi Multimodal Neural Machine Translation
تحسين اللغة الإنجليزية إلى الهندية الترجمة الآلية العصبية متعددة الوسائط
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الترجمة الآلية تؤدي الترجمة الآلية من لغة طبيعية إلى أخرى. تكمن ترجمة الآلات العصبية بمهارة أحدث في الترجمة الآلية، لكنها تتطلب بيانات تدريبية كافية، وهي مشكلة شديدة لترجمة أزواج لغة الموارد المنخفضة. يتم تقديم مفهوم Multimodal في الترجمة الآلية العصبية (NMT) عن طريق دمج الميزات النصية مع ميزات مرئية لتحسين ترجمة الزوج منخفض الموارد. WAT2021 (ورشة العمل حول الترجمة الآسيوية 2021) تنظم مهمة مشتركة من الترجمة متعددة الوسائط للإنجليزية إلى الهندية. لقد شاركنا نفس الشيء مع اسم الفريق CNLP-NITS-PP في طلبين: متعددة الوسائط والنصية فقط NMT. يحقق هذا العمل في حقن أزواج العبارة عن طريق نهج تكبير البيانات ويحمل تحسين عملنا السابق في Wat2020 في نفس المهمة في كل من NMT النصي فقط و Multimodal NMT. لقد حققنا المرتبة الثانية على مجموعة اختبار التحدي للغة الإنجليزية إلى الهندية الترجمة متعددة الوسائط حيث تقييم ثنائي اللغة من النتيجة 39.28، درجة التقييم البدياسية بديهية سهلة الاستخدام (RIBES) 0.792097، ومقاييس كفاية الطلاقة (AMFM) 0.830230 على التوالي وبعد
Machine translation performs automatic translation from one natural language to another. Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation. The concept of multimodal is introduced in neural machine translation (NMT) by merging textual features with visual features to improve low-resource pair translation. WAT2021 (Workshop on Asian Translation 2021) organizes a shared task of multimodal translation for English to Hindi. We have participated the same with team name CNLP-NITS-PP in two submissions: multimodal and text-only NMT. This work investigates phrase pairs injection via data augmentation approach and attains improvement over our previous work at WAT2020 on the same task in both text-only and multimodal NMT. We have achieved second rank on the challenge test set for English to Hindi multimodal translation where Bilingual Evaluation Understudy (BLEU) score of 39.28, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.792097, and Adequacy-Fluency Metrics (AMFM) score 0.830230 respectively.
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