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Register a new userActive Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages
التعلم النشط للترجمة الموازية على نطاق واسع للنص المقيد في لغات الموارد المنخفضة
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نترجم نص مغلق معروف مقدما ومتوفر في العديد من اللغات في لغة موارد جديدة منخفضة للغاية. تعتمد معظم جهود الترجمة البشرية اتباع نهج بوابة لترجمة الصفحات / الفصول على التوالي، والتي قد لا تناسب الترجمة الآلية. قارنا النهج القائم على الجزء الذي يحسن التماسك النص محليا مع نهج أخذ العينات العشوائية التي تزيد من تغطية النص على مستوى العالم. تظهر نتائجنا أن نهج أخذ العينات العشوائية يؤدي بشكل أفضل. عند التدريب على كوربوس البذور من ~1000 خطوط من الكتاب المقدس والاختبار على بقية الكتاب المقدس (~30،000 خطوط)، يعطي أخذ العينات العشوائية مكسب أداء من +11.0 بلو باستخدام اللغة الإنجليزية كمورد منخفضة مقلدة، و +4.9 بلو باستخدام Pokomchi الشرقية، لغة المايا. علاوة على ذلك، نقارن ثلاث طرق لتحديث نماذج الترجمة الآلية مع زيادة كمية البيانات التي تم تحريرها البشرية من خلال التكرارات. نجد أن إضافة بيانات تم تحريرها حديثا إلى التدريب بعد تحديث المفردات دون الإشراف الذاتي يؤدي الأفضل. نقترح خوارزمية للإنسان والآلة للعمل معا بسلاسة لترجمة نص مغلق إلى لغة موارد منخفضة للغاية.
We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portionbased approach to translate consecutive pages/chapters in order, which may not suit machine translation. We compare the portion-based approach that optimizes coherence of the text locally with the random sampling approach that increases coverage of the text globally. Our results show that the random sampling approach performs better. When training on a seed corpus of ∼1,000 lines from the Bible and testing on the rest of the Bible (∼30,000 lines), random sampling gives a performance gain of +11.0 BLEU using English as a simulated low resource language, and +4.9 BLEU using Eastern Pokomchi, a Mayan language. Furthermore, we compare three ways of updating machine translation models with increasing amount of human post-edited data through iterations. We find that adding newly post-edited data to training after vocabulary update without self-supervision performs the best. We propose an algorithm for human and machine to work together seamlessly to translate a closed text into a severely low resource language.
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