@unpublished{huang2017neural,
author = {Huang, Po-Sen and Wang, Chong and Zhou, Denny and Deng, Li},
title = {Neural Phrase-Based Machine Translation},
year = {2017},
month = {June},
abstract = {In this paper, we propose Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences through Sleep-WAke Networks (SWAN), a recently proposed segmentationbased sequence modeling method. To alleviate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Our experiments show that NPMT achieves state-of-the-art results on IWSLT 2014 German-English translation task without using any attention mechanisms. We also observe that our method produces meaningful phrases in the output language.},
url = {http://approjects.co.za/?big=en-us/research/publication/neural-phrase-based-machine-translation/},
note = {arXiv preprint},
}