Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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摘要：这篇论文首次提出了RNN Encoder–Decoder模型，RNN Encoder将符号序列转换成固定长度矢量表示，RNN Decoder将矢量表示解码为另一序列的符号。模型中的Encoder和Decoder通过联合训练以最大化给定源序列的目标序列的条件概率。经验发现统计机器翻译系统的性能通过使用由RNN Encoder–Decoder计算的短语对的条件概率作为现有对数线性模型中的附加特征来改进。定性地，提出的RNN Encoder–Decoder模型可以学习短语的语义和语法的有意义的表示。
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- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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