Machine learning
注意力机制
注意力机制由Bahdanau、Cho和Bengio于2015年提出，同年由Luong、Pham和Manning改进，它允许序列解码器动态学习在每个步骤中应该关注编码器的哪些输出。在Transformer之前，它通过将模型从将整个输入压缩成一个固定向量中解放出来，显著提高了机器翻译的质量。
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来源
- Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗
- Luong, M.T., Pham, H. & Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. EMNLP, 1412–1421. DOI: 10.18653/v1/D15-1166 ↗
如何引用本页
ScholarGate. (2026, June 1). Attention Mechanism (Bahdanau / Luong Attention). ScholarGate. https://scholargate.app/zh/deep-learning/attention-mechanism
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