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Register a new userComparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction
تحليل الأخطاء المقارنة في نماذج الحالة العصبية والمتوقعة لتحويل مستوى الطابع
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تقليديا، تم حل مشاكل نقل مستوى الأحرف مع طرازات الحالة المحدودة المصممة لتشفير المعرفة الهيكلية واللغوية بالعملية الأساسية، في حين أن النهج الحديثة تعتمد على قوة ومرونة نماذج التسلسل إلى التسلسل مع الاهتمام.التركيز على سيناريو التعلم الأقل استكشاف غير مخالفات، قارن الفصولتين النموذجيين جنبا إلى جنب وتجد أنهم يميلون إلى جعل أنواع مختلفة من الأخطاء حتى عند تحقيق أداء مماثل.نقوم بتحليل توزيعات فئات الأخطاء المختلفة باستخدام مهامين غير مدفوعين كمثبتين: تحويل النص الرباني بشكل غير رسمي إلى البرنامج النصي الأصلي لغته (للروسية والعربية وكانيا) وترجم بين زوج من اللغات ذات الصلة عن كثب (الصربية والبوسني).أخيرا، نقوم بالتحقيق في كيفية دمج نماذج الحالة المحدودة والتسلسل في وقت فك التشفير يؤثر على الإخراج الكمي والنوعي.
Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention. Focusing on the less explored unsupervised learning scenario, we compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance. We analyze the distributions of different error classes using two unsupervised tasks as testbeds: converting informally romanized text into the native script of its language (for Russian, Arabic, and Kannada) and translating between a pair of closely related languages (Serbian and Bosnian). Finally, we investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively.
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