Improving Statistical Machine Translation with Word Class Models

Automatically clustering words from a mono-lingual or bilingual training corpus into classes is a widely used technique in statisti-cal natural language processing. We present a very simple and easy to implement method for using these word classes to improve trans-lation quality. It can be applied across differ-ent machine translation paradigms and with arbitrary types of models. We show its ef-ficacy on a small German→English and a larger French→German translation task with both standard phrase-based and hierarchical phrase-based translation systems for a com-mon set of models. Our results show that with word class models, the baseline can be im-proved by up to 1.4 % BLEU and 1.0 % TER on the French→German task and 0.3 % BLEU and 1.1 % TER on the German→English task.