EMNLP 2017 NMT with word prediction
文章鏈接：https://arxiv.org/abs/1708.01771 概述： 這篇文章針對傳統的seq2seq模型decoder端的改進。 針對解決的問題： 1.傳統的seq2seq模型，encoder端通過一個雙向的LSTM或者GRU，生成一個固定維度的向量用來表示源端信息，我們用這個向量initial_state來表示源端的信息。因此源端所形成的initial_state在整個翻譯過程中
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相關文章
- 1. 論文解讀：Multimodal Machine Translation with Embedding Prediction
- 2. 【論文解讀 EMNLP 2019】Joint Event and Temporal RE with Shared Representations and Structured Prediction
- 3. TensorFlow NMT的詞嵌入(Word Embeddings)
- 4. 【論文解讀 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