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Register a new userSecoco: Self-Correcting Encoding for Neural Machine Translation
SECOCO: ترميز تصحيح ذاتي للترجمة الآلية العصبية
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تقدم هذه الورقة ترميز تصحيح ذاتي (SECOCO)، وهو إطار يتعامل بشكل فعال مع المدخلات الصاخبة للترجمة الآلية العصبية القوية عن طريق إدخال تنبؤ تصحيح ذاتي.تختلف عن الأساليب القوية السابقة، تمكن SECOCO NMT من تصحيح المدخلات الصاخبة بشكل صريح وحذف أخطاء محددة في وقت واحد مع عملية فك تشفير الترجمة.SECOCO قادرة على تحقيق تحسينات كبيرة على خطوط أساس قوية على مجموعتين لاختبار العالم الحقيقي ومجموعة بيانات معيار WMT مع إمكانية الترجمة الترجمة جيدة.سنجعل كودنا ومجموعات البيانات متاحة للجمهور قريبا.
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.
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