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Register a new userRobust Open-Vocabulary Translation from Visual Text Representations
الترجمة المتفوعة المتفرعة قوية من تمثيلات نصية مرئية
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تحتوي نماذج الترجمة الآلية على مفردات منفصلة واستخدام تقنيات تجزئة الكلمات الفرعية لتحقيق المفردات المفتوحة.يعتمد هذا النهج على تسلسل Unicode متسق وصحيح، ويجعل النماذج عرضة للتدهور من الأنواع المشتركة من الضوضاء والاختلاف.بدافع من متانة معالجة اللغات البشرية، نقترح استخدام تمثيلات نصية بصرية، والذي يتم الاستغناء عن مجموعة محدودة من Adgeddings النصية لصالح المفردات المستمرة التي تم إنشاؤها عن طريق معالجة النص المنصوص عليه بصريا مع Windows Sliding Windows.نظهر أن النماذج باستخدام نهج تمثيلات النص المرئي أو مطابقة أداء النماذج النصية التقليدية على مجموعات البيانات الصغيرة والأكبر.الأهم من ذلك، الأهم من ذلك، توضح النماذج ذات المدينات البصرية متانة كبيرة لأنواع الضوضاء المتنوعة، وتحقيق على سبيل المثال، 25.9 بلو على شخصية مسموعة باللغة الألمانية - المهمة الإنجليزية حيث تتحلل نماذج الكلمات الفرعية إلى 1.9.
Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an open vocabulary.' This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German--English task where subword models degrade to 1.9.
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