A Projection Extension Algorithm for Statistical Machine Translation

Abstract

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 high-precision word alignment. The system performance is significantly increased by applying a novel block extension algorithm using an additional high-recall 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.