Minimum Translation Modeling with Recurrent Neural Networks
Yuening Hu, Michael Auli, Qin Xiang Gao, Jianfeng Gao · 2014
We introduce recurrent neural networkbased 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 highorder 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 trained 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.