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attention model--Neural machine translation by jointly learning to align and translate論文解讀
時間 2021-01-02
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nlp
attention
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attention模型基本框圖 上面的兩幅圖已經把attention model表示出來了,下面我再以文字加以描述。 爲什麼需要attention 傳統的encoder-decoder模型大致如下圖所示: 可以看出傳統模型把句子的所有語義信息濃縮爲一箇中間語義C,每一個輸出的y都是利用了同一個C,所以輸出的每個y都利用了相同的語義信息C,輸入的每個單詞對於不同的輸出y都有相同的權重,並沒有引入注意
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相關文章
1.
論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
2.
【論文閱讀】Neural Machine Translation By Jointly Learning To Align and Translate
3.
【論文閱讀】Neural Machine Translation by Jointly Learning to Align and Translate
4.
Neural Machine Translation by Jointly Learning to Align and Translate
5.
NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
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論文閱讀《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》
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8.
論文閱讀:《Neural Machine Translation by Jointly Learning to Align and Translate》
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