Name-aware language model adaptation and sparse features for statistical machine translation

We propose approaches improving statistical machine translation (SMT) performance, by developing name-aware language model adaptations and sparse features, in addition to extracting name-aware translation grammar and rules, adding name phrase table, and name translation driven decoding. Chinese-English translation experiments showed that our proposed approaches produce an absolute gain of +2.3 BLEU on top of our previous high-performing, name-aware machine translation system.