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論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
時間 2020-12-24
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1. weakness of simple RNNenc Architecture 傳統的neural machine translation 大都是基於encoder-decoder模型,即通過一個encoder將源句子編碼成一個固定長度的向量表示,再使用decoder將這個向量作爲輸入,然後獲得輸出。這樣的話,Encoder需要將整個句子編碼到一個固定長度的向量,如果句子長度不大,還O
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相關文章
1.
attention model--Neural machine translation by jointly learning to align and translate論文解讀
2.
Neural Machine Translation by Jointly Learning to Align and Translate
3.
NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
4.
Neural machine translation by jointly learning to align and translate論文閱讀筆記
5.
Neural Machine Translation by Jointly Learning to Align and....
6.
論文閱讀《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》
7.
論文閱讀:《Neural Machine Translation by Jointly Learning to Align and Translate》
8.
【論文閱讀】Neural Machine Translation By Jointly Learning To Align and Translate
9.
【論文閱讀】Neural Machine Translation by Jointly Learning to Align and Translate
10.
對Neural Machine Translation by Jointly Learning to Align and Translate論文的詳解
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