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Register a new userBandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
لا تتبع قطاع الطرق القواعد: موازنة ترجمة آلة متعددة الأوجين مع قطاع الطرق المسلحة
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غالبا ما يتم الحصول على بيانات التدريب للترجمة الآلية (MT) من العديد من الشركات الكبيرة التي هي متعددة الأوجه في الطبيعة، على سبيل المثالتحتوي على محتويات من مجالات متعددة أو مستويات مختلفة من الجودة أو التعقيد.بطبيعة الحال، لا تحدث هذه الجوانب بتردد متساو ولا هي نفسها نفسها بنفس القدر لسيناريو الاختبار في متناول اليد.في هذا العمل، نقترح تحسين هذا التوازن بشكل مشترك مع معلمات نموذج MT لتخفيف مطوري النظام من تصميم الجدول اليدوي.يتم تدريب عصري متعدد المسلح على الاختيار ديناميكيا بين الجوانب بطريقة مفيدة لنظام MT.نقيمها على ثلاثة تطبيقات مختلفة متعددة الأوجه: موازنة البيانات النسبية والبيانات التدريبية الطبيعية، أو البيانات من مجالات متعددة أو أزواج متعددة اللغات.نجد أن تعلم الفرعيد يؤدي إلى أنظمة MT تنافسية عبر المهام، ويقدم تحليلنا رؤى في استراتيجياته المستفادة ومجموعات البيانات الأساسية.
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or different levels of quality or complexity. Naturally, these facets do not occur with equal frequency, nor are they equally important for the test scenario at hand. In this work, we propose to optimize this balance jointly with MT model parameters to relieve system developers from manual schedule design. A multi-armed bandit is trained to dynamically choose between facets in a way that is most beneficial for the MT system. We evaluate it on three different multi-facet applications: balancing translationese and natural training data, or data from multiple domains or multiple language pairs. We find that bandit learning leads to competitive MT systems across tasks, and our analysis provides insights into its learned strategies and the underlying data sets.
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