Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

时间 2021-01-02

标签论文机器翻译 nlp 人工智能神经网络繁體版

原文原文链接

预备知识： RNN：循环神经网络处理序列化数据，一般用于多输入多输出，数据之间存在关联性 U：输入层到隐藏层的权重矩阵 V：隐藏层到输出层的权重矩阵 W：隐藏层S不仅仅取决于当前这次的的输入x，还取决于上一次隐藏层的值St-1，权重矩阵W就是隐藏层上一次的值作为这一次输入的权重。用公式表示如下： Ot=g(V*St) St=f(U*Xt+

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1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
2. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
3. 翻译：Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
4. Paper：Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
5. Learning Phrase Representations using RNN Encoder–Decoder for...
6. 2014-Learning Phrase Representations using RNN Encoder–Decoder
7. (24) GRU & S2S：Learning Phrase Representations using RNN Encoder–Decoder for SMT
8. Statistical Machine Translation Tutorial Reading
9. Seq2Seq系列（一）：RNN Encoder-Decoder
10. Neural Machine Translation by Jointly Learning to Align and....
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1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
2. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
3. 翻译：Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
4. Paper：Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
5. Learning Phrase Representations using RNN Encoder–Decoder for...
6. 2014-Learning Phrase Representations using RNN Encoder–Decoder
7. (24) GRU & S2S：Learning Phrase Representations using RNN Encoder–Decoder for SMT
8. Statistical Machine Translation Tutorial Reading
9. Seq2Seq系列（一）：RNN Encoder-Decoder
10. Neural Machine Translation by Jointly Learning to Align and....

>>更多相关文章<<