A Phrase-Based Unigram Model for Statistical Machine Translation 
 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models . The units of translation are blocks - pairs of phrases . During decoding , we use a block unigram model and a word-based trigram language model . During training , the blocks are learned from source interval projections using an underlying word alignment . We show experimental results on block selection criteria based on unigram counts and phrase length. 