Fine-Grained Attention Mechanism for Neural Machine Translation
Fine-Grained Attention Mechanism for Neural Machine Translation 在這篇文章中作者提出了一種fine-grained的注意力機制即每一維的context vector將獲得一個單獨的注意力得分。 作者證明了這種做法的合理性 通過Choi et al.(2017)的文章，對詞向量的維度進行contextualization發現詞向量的每一
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- 1. Fine-Grained Attention Mechanism for Neural Machine Translation 論文筆記
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- 3. Paper：Effective Approaches to Attention-based Neural Machine Translation
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- 7. 論文解讀：Selective Attention for Context-aware Neural Machine Translation
- 8. Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings 論文總結
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相關文章
- 1. Fine-Grained Attention Mechanism for Neural Machine Translation 論文筆記
- 2. 神經網絡機器翻譯Neural Machine Translation: Attention Mechanism
- 3. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 4. [EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
- 5. 【CS224n】Neural Machine Translation with Seq2Seq
- 6. Seq2seq模型詳解（attention mechanism、evaluation methods 、Curriculum Learning、Machine Translation）
- 7. 論文解讀：Selective Attention for Context-aware Neural Machine Translation
- 8. Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings 論文總結
- 9. CS224n-Lecture8-Machine Translation, Seq2Seq and Attention
- 10. Attention mechanism總結