Machine learning
序列到序列模型
序列到序列（Seq2Seq）模型由 Sutskever、Vinyals 和 Le 以及 Cho 及其同事于 2014 年提出，是一种编码器-解码器神经网络，它将可变长度的输入序列映射到可变长度的输出序列。它是机器翻译、文本摘要、对话系统和代码生成的基础。
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来源
- Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗
- Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP, 1724–1734. DOI: 10.3115/v1/D14-1179 ↗
如何引用本页
ScholarGate. (2026, June 1). Sequence-to-Sequence (Seq2Seq) Encoder-Decoder Model. ScholarGate. https://scholargate.app/zh/deep-learning/seq2seq
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