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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 phrasebased 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 high-precision word alignment. The system performance is significantly increased by applying a novel block extension algorithm using an additional highrecall word alignment. The blocks are further filtered using unigram-count selection criteria. The system has been successfully test on a Chinese-English and an Arabic-English translation task.
Christoph Tillmann (Wed,) studied this question.