方法证据记录
Attention Mechanism
The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Attention Mechanism (Bahdanau / Luong Attention)
分类方法记录 · ml-model / deep-learning
- Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. · URL
- 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
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