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Facebook AI's WMT21
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وصفنا تقديم نموذج Facebook متعدد اللغات إلى المهمة المشتركة WMT2021 على ترجمة الأخبار. نشارك في 14 لغة لغة: الإنجليزية من وإلى جمهورية التشيك، الألمانية، الهوسا، الأيسلاندية واليابانية والروسية والصينية. لتطوير النظم التي تغطي كل هذه الاتجاهات، نركز على نماذج متعددة اللغات. نحن نستخدم البيانات من جميع المصادر المتاحة --- WMT، استخراج البيانات واسعة النطاق، وخلفه في المجال --- لإنشاء خطوط أساسية عالية الجودة ثنائية اللغة ومهدي اللغات. بعد ذلك، نقوم بالتحقيق في استراتيجيات لتحجيم حجم النموذج متعدد اللغات، بحيث يحتوي نظام واحد على قدر كاف لتمثيلات عالية الجودة لجميع اللغات الثمانية. تقدمنا النهائي لدينا هو مجموعة من نماذج الترجمة متعددة اللغات كثيفة ومتفجار، تليها Finetuning على بيانات الأخبار داخل المجال وإعادة تأهب القناة الصاخبة. مقارنة بتقديم الطلبات في العام السابق، قام نظامنا متعدد اللغات بتحسين جودة الترجمة على جميع الاتجاهات اللغوية، مع تحسين متوسط 2.0 بلو. في مهمة WMT2021، يحتل نظامنا المرتبة الأولى في 10 اتجاهات بناء على التقييم التلقائي.
We describe Facebook's multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models. We utilize data from all available sources --- WMT, large-scale data mining, and in-domain backtranslation --- to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all eight languages. Our final submission is an ensemble of dense and sparse Mixture-of-Expert multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year's winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.
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