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N-gram Language model plays an important role in statistical machine translation. Traditional methods adopt perplexity to evaluate language models, while this metric does not consider the characteristics of statistical machine translation. In this paper, we propose a novel method, namely bag-of-words decoding, to evaluate n-gram language models in phrase-based statistical machine translation. As compared...
This paper proposed a novel reordering model based on the reordering of source language chunks. This model is used as a preprocessing step of phrase-based translation models and could be well integrated with them. At the same time, as a chunk-based model, syntax information could be concerned in the process of reordering while the entire parsing of the source sentence is not required. Two experiments...
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