@inproceedings{hu2014minimum,
author = {Hu, Yuening and Auli, Michael and Gao, Qin and Gao, Jianfeng},
title = {Minimum Translation Modeling with Recurrent Neural Networks},
year = {2014},
month = {April},
abstract = {We introduce recurrent neural network based Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilingual contexts. Traditional back-off n-gram models suffer under the sparse nature of MTUs which makes estimation of high order sequence models challenging. We tackle the sparsity problem by modeling MTUs both as bags-of-words and as a sequence of individual source and target words. Our best results improve the output of a phrase-based statistical machine translation system rained on WMT 2012 French-English data by up to 1.5 BLEU, and we outperform the traditional n-gram based MTU approach by up to 0.8 BLEU.},
publisher = {European Chapter of the ACL (EACL)},
url = {http://approjects.co.za/?big=en-us/research/publication/minimum-translation-modeling-with-recurrent-neural-networks/},
}