An Efficient Phrase-to-Phrase Alignment Model for Arbitrarily Long Phrase and Large Corpora

Most statistical machine translation (SMT) systems use phrase-to-phrase translations to capture local context information, leading to better lexical choices and more reliable word reordering. Long phrases capture more contexts than short phrases and result in better translation qualities. On the other hand, the increasing amount of bilingual data poses serious problems for storing all possible phrases. In this paper, we describe a novel phrase-to-phrase alignment model which allows for arbitrarily long phrases and works for very large bilingual corpora. This model is very efficient in both time and space and the resulting translations are better than the state-of-the-art systems.