論文閱讀：ByteNet, Neural Machine Translation in Linear Time
Neural Translation Model 給定源語言 string s , 通過網絡模型來估計目標語言string t 的概率分佈 p(t|s) 。 與PixelCNN類似， t 的聯合概率分佈可以通過鏈式法則轉化爲連續的 p(ti|t<i,s) 條件概率的乘積。 strings通常是各自語言中的句子，string中的每一個token則是字母（或者單詞）。 模型包括以下兩個部分：
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相關文章
- 1. 論文閱讀——Toward Making the Most of Context in Neural Machine Translation
- 2. 【論文閱讀】Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
- 3. 【論文閱讀】Neural Machine Translation By Jointly Learning To Align and Translate
- 4. Neural machine translation by jointly learning to align and translate論文閱讀筆記
- 5. 論文 Non-Parametric Adaptation for Neural Machine Translation閱讀筆記
- 6. 【論文】on the properties of neural machine translation :Encoder-decoder approaches 閱讀筆記
- 7. 論文閱讀——Unsupervised Neural Machine Translation with Indirect Supervision
- 8. 論文閱讀——Modeling Future Cost for Neural Machine Translation
- 9. 論文閱讀《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》
- 10. 論文閱讀——Robust Unsupervised Neural Machine Translation with Adversarial Training