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Register a new userBayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation
التعلم Meta النموذجي النموذجي مع النواة ذات القيمة المصفوفة لتقدير الجودة
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يتم تدريب معظم نماذج تقدير الجودة الحالية (QE) للترجمة الآلية وتقييمها في بيئة إشراف بالكامل تتطلب كميات كبيرة من بيانات التدريب المسمى. ومع ذلك، يمكن أن تكون الحصول على البيانات المسمى باهظة الثمن وتستغرق وقتا طويلا. بالإضافة إلى ذلك، قد تتعرض بيانات الاختبار التي سيتم التعرض لها نموذج QE المنشور قد تختلف عن بيانات التدريب الخاصة بها بطرق مهمة. على وجه الخصوص، غالبا ما يتم تصنيف عينات التدريب من خلال مجموعة واحدة أو مجموعة صغيرة من المعلقين، والتي قد تختلف تصورات جودة الترجمة واحتياجاتها بشكل كبير من هؤلاء المستخدمين النهائيين، الذين سيعملون التنبؤات في الممارسة العملية. وبالتالي، من المرغوب فيه أن تكون قادرا على التكيف مع نماذج QE بكفاءة إلى بيانات المستخدم الجديدة مع بيانات الإشراف المحدودة. لمعالجة هذه التحديات، نقترح نهجا لتعليم التعلم بايزي لتكييف نماذج QE لاحتياجات وتفضيلات كل مستخدم مع إشراف محدود. لتعزيز الأداء، فإننا نقترح كذلك امتدادا إلى نهج التعلم التلوي بايز بين الفنادق التي تستخدم نواة مصفوفة ذات قيمة لتعلم التلوي بايزي لتقدير الجودة. توضح التجارب المعنية ببيانات عدد متفاوت من المستخدمين والخصائص اللغوية أن نهج التعلم التلوي المقترح يقدم أداء تنبؤي محسن في كل من إعدادات الإشراف المحدودة والكامل.
Most current quality estimation (QE) models for machine translation are trained and evaluated in a fully supervised setting requiring significant quantities of labelled training data. However, obtaining labelled data can be both expensive and time-consuming. In addition, the test data that a deployed QE model would be exposed to may differ from its training data in significant ways. In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of end-users, who will employ predictions in practice. Thus, it is desirable to be able to adapt QE models efficiently to new user data with limited supervision data. To address these challenges, we propose a Bayesian meta-learning approach for adapting QE models to the needs and preferences of each user with limited supervision. To enhance performance, we further propose an extension to a state-of-the-art Bayesian meta-learning approach which utilizes a matrix-valued kernel for Bayesian meta-learning of quality estimation. Experiments on data with varying number of users and language characteristics demonstrates that the proposed Bayesian meta-learning approach delivers improved predictive performance in both limited and full supervision settings.
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