基于PyTorch实现Seq2Seq + Attention的英汉Neural Machine Translation
NMT(Neural Machine Translation)基于神经网络的机器翻译模型效果愈来愈好，还记得大学时代Google翻译效果仍是差强人意，近些年来使用NMT后已基本能知足非特殊需求了。目前NMT的主流模型是采用Seq2Seq + Attention架构，本文基于PyTorch实现一个小型的英文到中文的翻译系统。git 一、数据集 数据集及所有代码下载连接：百度网盘，提取码：sauzgi
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相关文章
- 1. lecture8 - Machine Translation Seq2Seq and Attention
- 2. CS224n-Lecture8-Machine Translation, Seq2Seq and Attention
- 3. 【cs224n-7】Machine Translation, Seq2Seq and Attention
- 4. 【CS224n】Neural Machine Translation with Seq2Seq
- 5. Seq2Seq(Attention)的PyTorch实现
- 6. cs224笔记：Lecture 8 Machine Translation, Seq2Seq and Attention
- 7. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 8. Fine-Grained Attention Mechanism for Neural Machine Translation
- 9. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 10. 论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation