Abstract Kneser-Ney (1995) smoothing and its variants are generally recognized as having the best perplexity of any known method for estimating N-gram language models. Kneser-Ney smoothing, however, requires nonstandard N-gram counts for the lowerorder models used to smooth the highestorder model. For some applications, this makes Kneser-Ney smoothing inappropriate or inconvenient. In this paper, we introduce a new smoothing method based on ordinary counts that outperforms all of the previous ordinary-count methods we have tested, with the new method eliminating most of the gap between Kneser-Ney and those methods.
Moore and Quirk REF described smoothing method for N-gram language models based on ordinary counts for generation language models which can use for quality estimation task.
Improved Smoothing for N-gram Language Models Based on Ordinary Counts
ACL-IJCNLP: Short Papers
Computer Science