Phrase-based SMT(Statistical Machine Translation) models have advanced the state of the art in machine translation by expanding the basic unit of translation from words to phrases, which allows the local reordering of words and translation of multi-word expressions (Chiang, 2007) (Koehn et al., 2003) (Och andNey, 2004).
phrase-based SMT techniques suffer from data sparseness problems, that is; unreliable translation probabilities of low frequency phrases and low coverage in that many phrases encountered at run-time are not observed in the training data.