We describe the statistical machine translation system developed at the National Research Council of Canada (NRC) for the Russian-English news translation task of the First Conference on Machine Translation (WMT 2016). Our submission is a phrase-based SMT system that tackles the morphological complexity of Russian through comprehensive use of lemmatization. The core of our lemmatization strategy is to use different views of Russian for different SMT components: word alignment and bilingual neural network language models use lemmas, while sparse features and reordering models use fully inflected forms. Some components, such as the phrase table, use both views of the source. Russian words that remain out-ofvocabulary (OOV) after lemmatization are transliterated into English using a statistical model trained on examples mined from the parallel training corpus. The NRC Russian-English MT system achieved the highest uncased BLEU and the lowest TER scores among the eight participants in WMT 2016.
['Chi-kiu Lo', 'Colin Cherry', 'George F. Foster', 'Darlene A. Stewart', 'Rabib Islam', 'Anna Kazantseva', 'Roland Kuhn']
NRC Russian-English Machine Translation System for WMT 2016