  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.
