EMNLP 2017 NMT with word prediction
文章链接：https://arxiv.org/abs/1708.01771 概述： 这篇文章针对传统的seq2seq模型decoder端的改进。 针对解决的问题： 1.传统的seq2seq模型，encoder端通过一个双向的LSTM或者GRU，生成一个固定维度的向量用来表示源端信息，我们用这个向量initial_state来表示源端的信息。因此源端所形成的initial_state在整个翻译过程中
相关文章
- 1. 论文解读：Multimodal Machine Translation with Embedding Prediction
- 2. 【论文解读 EMNLP 2019】Joint Event and Temporal RE with Shared Representations and Structured Prediction
- 3. TensorFlow NMT的词嵌入(Word Embeddings)
- 4. 【论文解读 EMNLP 2019 | TLNN】Event Detection with Trigger-Aware Lattice Neural Network
- 5. Dealing with Noise in Defect Prediction
- 6. Chapter 9 On-policy Prediction with Approximation
- 7. #Paper reading#DeepInf: Social Influence Prediction with Deep Learning
- 8. NLocalSAT: Boosting Local Search with Solution Prediction 2020-05-02
- 9. [NLP-CNN] Convolutional Neural Networks for Sentence Classification -2014-EMNLP
- 10. Word embeddings in 2017: Trends and future directions
- 更多相关文章...
- • XSLT
- • XSL-FO character 对象 - XSL-FO 教程
- • 算法总结-广度优先算法
- • 为了进字节跳动，我精选了29道Java经典算法题，带详细讲解
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文解读：Multimodal Machine Translation with Embedding Prediction
- 2. 【论文解读 EMNLP 2019】Joint Event and Temporal RE with Shared Representations and Structured Prediction
- 3. TensorFlow NMT的词嵌入(Word Embeddings)
- 4. 【论文解读 EMNLP 2019 | TLNN】Event Detection with Trigger-Aware Lattice Neural Network
- 5. Dealing with Noise in Defect Prediction
- 6. Chapter 9 On-policy Prediction with Approximation
- 7. #Paper reading#DeepInf: Social Influence Prediction with Deep Learning
- 8. NLocalSAT: Boosting Local Search with Solution Prediction 2020-05-02
- 9. [NLP-CNN] Convolutional Neural Networks for Sentence Classification -2014-EMNLP
- 10. Word embeddings in 2017: Trends and future directions