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Register a new userSmart-Start Decoding for Neural Machine Translation
فك التشفير الذكية للترجمة الآلية العصبية
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تعتمد معظم نماذج الترجمة الآلية العصبية الحالية ترتيب فك التشفير الرخيصي إما من اليسار إلى اليمين أو اليمين إلى اليسار.في هذا العمل، نقترح طريقة رواية تنفصل قيود أوامر فك التشفير هذه، تسمى فك تشفير الذكية.وبشكل أكثر تحديدا، تتوقع طريقةنا أولا كلمة متوسط.يبدأ فك شفرة الكلمات الموجودة على الجانب الأيمن من الكلمة المتوسطة ثم يولد كلمات على اليسار.نحن نقيم طريقة فك التشفير الذكية المقترحة على ثلاث مجموعات البيانات.تظهر النتائج التجريبية أن الطريقة المقترحة يمكن أن تتفوق بشكل كبير على النماذج الأساسية القوية.
Most current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left. In this work, we propose a novel method that breaks up the limitation of these decoding orders, called Smart-Start decoding. More specifically, our method first predicts a median word. It starts to decode the words on the right side of the median word and then generates words on the left. We evaluate the proposed Smart-Start decoding method on three datasets. Experimental results show that the proposed method can significantly outperform strong baseline models.
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Neural machine translation (NMT) models are data-driven and require large-scale training corpus. In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus.
However, this bears the risk of catastrophic forgetting that the performance on the general domain is decreased drastically. In this work, we propose a new continual learning framework for NMT models. We consider a scenario where the training is comprised of multiple stages and propose a dynamic knowledge distillation technique to alleviate the problem of catastrophic forgetting systematically. We also find that the bias exists in the output linear projection when fine-tuning on the in-domain corpus, and propose a bias-correction module to eliminate the bias. We conduct experiments on three representative settings of NMT application. Experimental results show that the proposed method achieves superior performance compared to baseline models in all settings.
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