论文解读：Sequence to Sequence Mixture Model for Diverse Machine Translation
论文解读：Sequence to Sequence Mixture Model for Diverse Machine Translation 机器翻译是自然语言处理中比较热门的研究任务，在深度学习背景下，通过神经网络搭建的机器翻译也称为当今主流方式。在解决机器翻译过程中需要解决诸多问题，例如原始句子的语义表征、句子对齐、集束搜索、未知词、漏译过译等。而对于这种序列到序列的任务中，通常有诸多策
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相关文章
- 1. CVPR 2018 Convolutional Sequence to Sequence Model for Human Dynamics 论文解读
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