  Although Perplexity is a widely used performance metric for language models,
the values are highly dependent upon the number of words in the corpus and is
useful to compare performance of the same corpus only. In this paper, we
propose a new metric that can be used to evaluate language model performance
with different vocabulary sizes. The proposed unigram-normalized Perplexity
actually presents the performance improvement of the language models from that
of simple unigram model, and is robust on the vocabulary size. Both theoretical
analysis and computational experiments are reported.
