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【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
時間 2020-12-24
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attention
seq2seq
NLP
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原文鏈接
論文鏈接 本文針對NMT任務使用的attention機制提出兩種結構,global attention將attention作用於全部輸入序列,local attention每個時間步將attention作用於輸入序列的不同子集。前者被稱爲soft attention,其原理和論文1一樣,後者是hard attention和soft attention的結合,該文通過context vecto
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論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
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相關文章
1.
論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
2.
Effective Approaches to Attention-based Neural Machine Translation論文筆記
3.
[EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
4.
Effective Approaches to Attention-based Neural Machine Translation 學習筆記
5.
Paper:Effective Approaches to Attention-based Neural Machine Translation
6.
【論文】on the properties of neural machine translation :Encoder-decoder approaches 閱讀筆記
7.
多標籤分類:Effective Approaches to Attention-based Neural Machine Translation
8.
Effective Approaches to Attention-based Neural Machine Translation之每日一篇
9.
論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
10.
Neural machine translation by jointly learning to align and translate論文閱讀筆記
>>更多相關文章<<