Linguistic Input Features Improve Neural Machine Translation
输入语言特征改进神经机器翻译1 目录 输入语言特征改进神经机器翻译1 目录 摘要 介绍 神经网络机器翻译 增加输入特征 语言学特征 词性还原 子标签 词法特征 POS标签和依赖标签 关于在子字模型中使用字符级功能 摘要 神经机器翻译最近取得了令人瞩目的成果，同时对外部语言信息的很少使用。在本文中，我们表明神经MT模型的强大学习能力不会使语言特征冗余; 它们可以很容易地结合起来，以进一步提高性能。
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相关文章
- 1. 【CS224n】Neural Machine Translation with Seq2Seq
- 2. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 3. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 4. Neural Machine Translation by Jointly Learning to Align and....
- 5. Fine-Grained Attention Mechanism for Neural Machine Translation
- 6. Neural Machine Translation by Jointly Learning to Align and Translate
- 7. Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
- 8. NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
- 9. 多标签分类：Google‘s Neural Machine Translation System
- 10. Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation