【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
論文鏈接 本文針對NMT任務使用的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 attention，其原理和論文1一樣，後者是hard attention和soft attention的結合，該文通過context vecto
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相關文章
- 1. 論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
- 2. Effective Approaches to Attention-based Neural Machine Translation論文筆記
- 3. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 4. Effective Approaches to Attention-based Neural Machine Translation 學習筆記
- 5. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 6. 【論文】on the properties of neural machine translation :Encoder-decoder approaches 閱讀筆記
- 7. 多標籤分類：Effective Approaches to Attention-based Neural Machine Translation
- 8. Effective Approaches to Attention-based Neural Machine Translation之每日一篇
- 9. 論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
- 10. Neural machine translation by jointly learning to align and translate論文閱讀筆記