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Register a new userDistributionally Robust Multilingual Machine Translation
ترجمة آلة متعددة اللغات قوية
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تعلم الترجمة الآلية العصبية متعددة اللغات (MNMT) ترجمة أزواج متعددة اللغات بنموذج واحد، يحتمل أن يحسن كل من الدقة وكفاءة الذاكرة للنماذج المنتشرة. ومع ذلك، فإن عدم اختلال البيانات الثقيلة بين اللغات يعوق النموذج من الأداء بشكل موحد عبر أزواج اللغة. في هذه الورقة، نقترح هدفا تعليميا جديدا ل MNMT بناء على التحسين القوي التويضي، مما يقلل من الخسارة المتوقعة الأسوأ في مجموعة أزواج اللغة. نوضح كذلك كيفية تحسين هذا الهدف من الناحية العملية للترجمة الكبيرة باستخدام مخطط أفضل استجابة مزخرف، وهو فعاليتان فعالة وتتحمل تكلفة حسابية إضافية ضئيلة مقارنة بقليل المخاطر التجريبية القياسية. نقوم بإجراء تجارب مكثفة على ثلاث مجموعات من اللغات من مجموعة بيانات وتظهر أن طريقتنا تتفوق باستمرار على أساليب خطية قوية من حيث المتوسط والأداء لكل لغة تحت كلا من إعدادات الترجمة متعددة إلى واحدة وواحدة متعددة.
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization. We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.
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