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Register a new userIITP at WAT 2021: System description for English-Hindi Multimodal Translation Task
IITP في WAT 2021: وصف النظام لمهمة الترجمة الإنجليزي الهندية متعددة الوسائط
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الترجمة الآلية العصبية (NMT) هي تكنولوجيا ترجمة آلية سائدة في الوقت الحاضر بسبب مرونةها التدريبية المتنقلة المحيرة.ومع ذلك، لا يزال NMT يكافح من أجل الترجمة بشكل صحيح في إعدادات الموارد المنخفضة خصيصا على أزواج اللغة البعيدة.طريقة واحدة للتغلب على ذلك هي استخدام المعلومات من طرائق أخرى إذا كانت متوفرة.الفكرة هي أنه على الرغم من الاختلافات في اللغات، فإن كل من المصدر والمتحدثين اللغوي المستهدف يرون نفس الشيء والتمثيل المرئي لكل من المصدر والهدف هو نفسه، والذي يمكن أن يساعد النظام بشكل إيجابي.يمكن أن تساعد المعلومات متعددة الوسائط نظام NMT لتحسين الترجمة عن طريق إزالة الغموض على بعض العبارات أو الكلمات.نحن نشارك في ورشة العمل الثامنة حول الترجمة الآسيوية (WAT - 2021) لمهمة الترجمة الإنجليزية العربية الهندية وتحقيق 42.47 و 37.50 نقطة بلو للتقييم والتحدي الفرعي، على التوالي.
Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.
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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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