A projection extension algorithm for statistical machine translation
Christoph Tillmann · 2003
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.