论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
上一篇attention介绍了Bahdanau等人提出的一种soft-attention模型。 Luong等人在此基础上提出了两种简单且有效的方式：全局(global)和局部(local)的方式。所谓的global，就是指每次都关注整个source sentence，而local则是在每个时间t，只关注一部分source sentence。 Luong等人提出的global类似于Bahdanau
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相关文章
- 1. 【论文笔记】Effective Approaches to Attention-based Neural Machine Translation
- 2. Effective Approaches to Attention-based Neural Machine Translation论文笔记
- 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 学习笔记
- 6. 论文笔记(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
- 7. 【论文】on the properties of neural machine translation :Encoder-decoder approaches 阅读笔记
- 8. 多标签分类：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 论文笔记