On the dynamic adaptation of stochastic language models

A simple and general scheme for the adaptation of stochastic language models to changing text styles is introduced. For each word in the running text, the adapted model is a linear combination of specific models, the interpolation parameters being estimated on the preceding text passage. Experiments on a 1.1-million English word corpus show the validity of the approach. The adaptation method improves a bigram language model by 10% in terms of test-set perplexity.<>