【论文笔记】Effective Approaches to Attention-based Neural Machine Translation

时间 2020-12-24

标签 attention seq2seq NLP 繁體版

原文原文链接

论文链接本文针对NMT任务使用的attention机制提出两种结构，global attention将attention作用于全部输入序列，local attention每个时间步将attention作用于输入序列的不同子集。前者被称为soft attention，其原理和论文1一样，后者是hard attention和soft attention的结合，该文通过context vecto

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1. 论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
2. Effective Approaches to Attention-based Neural Machine Translation论文笔记
3. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
4. Effective Approaches to Attention-based Neural Machine Translation 学习笔记
5. Paper：Effective Approaches to Attention-based Neural Machine Translation
6. 【论文】on the properties of neural machine translation :Encoder-decoder approaches 阅读笔记
7. 多标签分类：Effective Approaches to Attention-based Neural Machine Translation
8. Effective Approaches to Attention-based Neural Machine Translation之每日一篇
9. 论文笔记(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
10. Neural machine translation by jointly learning to align and translate论文阅读笔记
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1. 论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
2. Effective Approaches to Attention-based Neural Machine Translation论文笔记
3. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
4. Effective Approaches to Attention-based Neural Machine Translation 学习笔记
5. Paper：Effective Approaches to Attention-based Neural Machine Translation
6. 【论文】on the properties of neural machine translation :Encoder-decoder approaches 阅读笔记
7. 多标签分类：Effective Approaches to Attention-based Neural Machine Translation
8. Effective Approaches to Attention-based Neural Machine Translation之每日一篇
9. 论文笔记(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
10. Neural machine translation by jointly learning to align and translate论文阅读笔记

>>更多相关文章<<