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Smart-Start Decoding for Neural Machine Translation

فك التشفير الذكية للترجمة الآلية العصبية

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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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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