A Unigram Orientation Model for Statistical Machine Translation
IBM THOMAS J WATSON RESEARCH CENTER YORKTOWN HEIGHTS NY
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In this paper, we present a unigram segmentation model for statistical machine translation where the segmentation units are blocks pairs of phrases without internal structure. The segmentation model uses a novel orientation component to handle swapping of neighbor blocks. During training, we collect block unigram counts with orientation we count how often a block occurs to the left or to the right of some predecessor block. The orientation model is shown to improve translation performance over two models 1 no block re-ordering is used, and 2 the block swapping is controlled only by a language model. We show experimental results on a standard Arabic-English translation task.