In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our
method explicitly models the phrase structures in output sequences using
Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence
modeling method. To mitigate the monotonic alignment requirement of SWAN, we
introduce a new layer to perform (soft) local reordering of input sequences.
Different from existing neural machine translation (NMT) approaches, NPMT does
not use attention-based decoding mechanisms. Instead, it directly outputs
phrases in a sequential order and can decode in linear time. Our experiments
show that NPMT achieves superior performances on IWSLT 2014
German-English/English-German and IWSLT 2015 English-Vietnamese machine
translation tasks compared with strong NMT baselines. We also observe that our
method produces meaningful phrases in output languages.

本文提出神经短语机器翻译模型 (NPMT)，采用基于分段的序列建模方法 (SWAN) 显式建模翻译序列中的短语结构并引入新层实现局部重新排序 (Soft)。相比于基于注意力机制的 NMT 方法，NPMT 直接以顺序方式输出短语，因此可以在线性时间内解码，实验表明与强 NMT 基准模型相比，在 IWSLT 2014 德英 / 英德和 IWSLT 2015 英越机器翻译任务上表现优越，并且 NPMT 模型的输出翻译语言有意义。