We analyze the performance of smoothing methods for language modeling from the perspective of universal compression. We use existing asymptotic bounds on the performance of simple additive rules for compression of finite-alphabet memoryless sources to explain the empirical predictive abilities of additive smoothing techniques. We further suggest a smoothing method that overcomes some of the problems observed in previous approaches. The new method outperforms existing ones on the Wall Street Journal(WSJ) database for bigram and trigram models. We then suggest possible directions for future research. 1.

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We analyze smoothing algorithms from a universal-compression perspective. Instead of evaluating their performance on an empirical sample, we analyze their performance on the most inconvenient sample possible. Consequently the performance of the algorithm can be guaranteed even on unseen data. We show that universal compression bounds can explain the empirical performance of several smoothing methods. We also describe a new interpolated additive smoothing algorithm, and show that it has lower training complexity and better compression performance than existing smoothing techniques. Key words: Language modeling, universal compression, smoothing 1