Effective Approaches to Attention-based Neural Machine Translation之每日一篇
论文地址：http://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP/pdf/EMNLP166.pdf 概述： 本文针对NMT任务是用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
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相关文章
- 1. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 2. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 3. 【论文笔记】Effective Approaches to Attention-based Neural Machine Translation
- 4. 论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
- 5. Effective Approaches to Attention-based Neural Machine Translation 学习笔记
- 6. 多标签分类：Effective Approaches to Attention-based Neural Machine Translation
- 7. Effective Approaches to Attention-based Neural Machine Translation论文笔记
- 8. An Effective Approach to Unsupervised Machine Translation
- 9. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
- 10. 【CS224n】Neural Machine Translation with Seq2Seq