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Register a new userOn the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
على التكامل بين ما قبل التدريب والترجمة الخلفية للترجمة الآلية العصبية
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ما قبل التدريب (PT) والترجمة الخلفي (BT) هي طريقتان بسيطان وقويهما لاستخدام البيانات الأولية لتحسين الأداء النموذجي للترجمة الآلية العصبية (NMT).تأخذ هذه الورقة الخطوة الأولى للتحقيق في التكامل بين PT و BT.نقدم اثنين من المهام التحقيق الخاصة ب PT و BT على التوالي وتجد أن PT يساهم بشكل أساسي في وحدة التشفير أثناء قيام BT بتجلب المزيد من الفوائد إلى وحدة فك الترميز.تظهر النتائج التجريبية أن PT و BT مكملة بشكل جيد مع بعضها البعض، وإنشاء عروض أحدث على المعايير WMT16 الإنجليزية والرومانية والروسية.من خلال تحليلات واسعة النطاق على عصالة الجملة وتيرة الكلمة، فإننا نوضح أيضا أن الجمع بين الموسومة BT مع PT هو أكثر فائدة تكاملها، مما يؤدي إلى جودة ترجمة أفضل.شفرة المصدر متاحة بحرية في HTTPS://github.com/sunbowliu/ptvsbt.
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT). This paper takes the first step to investigate the complementarity between PT and BT. We introduce two probing tasks for PT and BT respectively and find that PT mainly contributes to the encoder module while BT brings more benefits to the decoder. Experimental results show that PT and BT are nicely complementary to each other, establishing state-of-the-art performances on the WMT16 English-Romanian and English-Russian benchmarks. Through extensive analyses on sentence originality and word frequency, we also demonstrate that combining Tagged BT with PT is more helpful to their complementarity, leading to better translation quality. Source code is freely available at https://github.com/SunbowLiu/PTvsBT.
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Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. However, in real-world scenarios, massive monolingual corpora do not exist for some extreme
ly low-resource languages such as Estonian, and UNMT systems usually perform poorly when there is not adequate training corpus for one language. In this paper, we first define and analyze the unbalanced training data scenario for UNMT. Based on this scenario, we propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance in this case. Experimental results on several language pairs show that the proposed methods substantially outperform conventional UNMT systems.
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