An Approach to N-Gram Language Model Evaluation in Phrase-Based Statistical Machine Translation

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 with perplexity, our approach has more remarkable correlation with translation quality measured by BLEU. Experimental results on NIST data sets demonstrate the effectiveness of our method.