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Register a new userHierarchical Transformer for Multilingual Machine Translation
محول هرمي للترجمة متعددة اللغات
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يحدد اختيار استراتيجية مشاركة المعلمات في نماذج الترجمة الآلية متعددة اللغات مدى استخدام مساحة المعلمة الأمثلة، وبالتالي، تؤثر مباشرة على جودة الترجمة النهائية.وقد اقترح مؤخرا مختارة من الأشجار اللغوية التي تظهر درجة الرعاية بين اللغات المختلفة، كما تم اقتراح النهج العام الجديد لمشاركة المعلمة في الترجمة متعددة اللغات في الترجمة متعددة اللغات.تتمثل الفكرة الرئيسية في استخدام هذه التسلسلات الهرمية لغوية الخبراء كأساس للهندسة المعمارية متعددة اللغات: كلما زادت اللغتين، كلما زاد عدد المعلمات التي يشاركونها.في هذا العمل، نختبر هذه الفكرة باستخدام بنية المحولات وإظهار أنه على الرغم من النجاح في العمل السابق هناك مشاكل متأصلة لتدريب هذه النماذج الهرمية.نوضح أنه في حالة اتباع استراتيجية التدريب المختارة بعناية، يمكن للهندسة الهيكل الهرمية تفوق النماذج ثنائية اللغة ونماذج متعددة اللغات مع مشاركة المعلمات الكاملة.
The choice of parameter sharing strategy in multilingual machine translation models determines how optimally parameter space is used and hence, directly influences ultimate translation quality. Inspired by linguistic trees that show the degree of relatedness between different languages, the new general approach to parameter sharing in multilingual machine translation was suggested recently. The main idea is to use these expert language hierarchies as a basis for multilingual architecture: the closer two languages are, the more parameters they share. In this work, we test this idea using the Transformer architecture and show that despite the success in previous work there are problems inherent to training such hierarchical models. We demonstrate that in case of carefully chosen training strategy the hierarchical architecture can outperform bilingual models and multilingual models with full parameter sharing.
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