Effective Approaches to Attention-based Neural Machine Translation之每日一篇
論文地址：http://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP/pdf/EMNLP166.pdf 概述： 本文針對NMT任務是用attention機制提出兩種結構，global attention將attention作用於全部輸入序列，local attention每個時間步將attention作用於輸入序列的不同子集。前者被稱爲soft
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相關文章
- 1. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 2. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 3. 【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
- 4. 論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
- 5. Effective Approaches to Attention-based Neural Machine Translation 學習筆記
- 6. 多標籤分類：Effective Approaches to Attention-based Neural Machine Translation
- 7. Effective Approaches to Attention-based Neural Machine Translation論文筆記
- 8. An Effective Approach to Unsupervised Machine Translation
- 9. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
- 10. 【CS224n】Neural Machine Translation with Seq2Seq