論文筆記：Beam Search Strategies for Neural Machine Translation
作者： Markus Freitag and Yaser Al-Onaizan 單位： IBM T.J. Watson Research Center 關鍵詞： Beam search; Pruning strategies 問題： 束搜索算法跟蹤k個狀態，而不僅僅只跟蹤一個。它從k個隨機生成的狀態開始，在每一步中都生成所有k個狀態的所有後繼者。如果這其中的任何一個後繼者是目標，那麼算法就會停止。
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相關文章
- 1. 論文閱讀：Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
- 2. 論文 Non-Parametric Adaptation for Neural Machine Translation閱讀筆記
- 3. Fine-Grained Attention Mechanism for Neural Machine Translation 論文筆記
- 4. Sampled Softmax 論文筆記：On Using Very Large Target Vocabulary for Neural Machine Translation
- 5. Neural machine translation by jointly learning to align and translate論文閱讀筆記
- 6. 【論文】on the properties of neural machine translation :Encoder-decoder approaches 閱讀筆記
- 7. [論文閱讀筆記] Massive Exploration of Neural Machine Translation Architectures
- 8. 論文筆記(attention 1)----NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
- 9. 【論文筆記】Effective Approaches to Attention-based Neural Machine Translation
- 10. 論文筆記(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation