論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
上一篇attention介紹了Bahdanau等人提出的一種soft-attention模型。 Luong等人在此基礎上提出了兩種簡單且有效的方式：全局(global)和局部(local)的方式。所謂的global，就是指每次都關注整個source sentence，而local則是在每個時間t，只關注一部分source sentence。 Luong等人提出的global類似於Bahdanau
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- 1. 【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
- 2. Effective Approaches to Attention-based Neural Machine Translation論文筆記
- 3. Paper：Effective Approaches to Attention-based Neural Machine Translation
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相關文章
- 1. 【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
- 2. Effective Approaches to Attention-based Neural Machine Translation論文筆記
- 3. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 4. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 5. Effective Approaches to Attention-based Neural Machine Translation 學習筆記
- 6. 論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
- 7. 【論文】on the properties of neural machine translation :Encoder-decoder approaches 閱讀筆記
- 8. 多標籤分類：Effective Approaches to Attention-based Neural Machine Translation
- 9. Effective Approaches to Attention-based Neural Machine Translation之每日一篇
- 10. Fine-Grained Attention Mechanism for Neural Machine Translation 論文筆記