1 research outputs found
Language Model-Driven Unsupervised Neural Machine Translation
Unsupervised neural machine translation(NMT) is associated with noise and
errors in synthetic data when executing vanilla back-translations. Here, we
explicitly exploits language model(LM) to drive construction of an unsupervised
NMT system. This features two steps. First, we initialize NMT models using
synthetic data generated via temporary statistical machine translation(SMT).
Second, unlike vanilla back-translation, we formulate a weight function, that
scores synthetic data at each step of subsequent iterative training; this
allows unsupervised training to an improved outcome. We present the detailed
mathematical construction of our method. Experimental WMT2014 English-French,
and WMT2016 English-German and English-Russian translation tasks revealed that
our method outperforms the best prior systems by more than 3 BLEU points.Comment: 11 pages, 3 figures, 7 table