N-gram posterior probabilities for statistical machine translation
Richard Zens, Hermann Ney · 2006
Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation.We will generalize this idea and introduce n-gram posterior probabilities and show how these can be used to improve translation quality.Additionally, we will introduce a sentence length model based on posterior probabilities.We will show significant improvements on the Chinese-English NIST task.The absolute improvements of the BLEU score is between 1.1% and 1.6%.