  Reordering is a challenge to machine translation (MT) systems. In MT, the
widely used approach is to apply word based language model (LM) which considers
the constituent units of a sentence as words. In speech recognition (SR), some
phrase based LM have been proposed. However, those LMs are not necessarily
suitable or optimal for reordering. We propose two phrase based LMs which
considers the constituent units of a sentence as phrases. Experiments show that
our phrase based LMs outperform the word based LM with the respect of
perplexity and n-best list re-ranking.
