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多標籤分類:Effective Approaches to Attention-based Neural Machine Translation
時間 2021-01-02
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論文地址:https://arxiv.org/pdf/1508.04025v3.pdf 寫在前面:本文《基於注意力的神經機器翻譯的有效方法》,在Seq2Seq架構的基礎上引入了Global Attention 和 Local Attention,爲基於Seq2Seq的多標籤學習提供新的思路。 摘要 通過在翻譯過程中選擇性地關注源句子的部分內容,attention 機制近來已被用於改進神經機器翻譯(
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相關文章
1.
[EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
2.
多標籤分類:Google‘s Neural Machine Translation System
3.
Paper:Effective Approaches to Attention-based Neural Machine Translation
4.
【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
5.
論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
6.
Effective Approaches to Attention-based Neural Machine Translation 學習筆記
7.
Effective Approaches to Attention-based Neural Machine Translation之每日一篇
8.
Effective Approaches to Attention-based Neural Machine Translation論文筆記
9.
An Effective Approach to Unsupervised Machine Translation
10.
【CS224n】Neural Machine Translation with Seq2Seq
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