This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.
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Graça et al. (2018) studied this question.
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