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The multigram model assumes that language can be described as the output of a memoryless source that emits variable-length sequences of words. The estimation of the model parameters can be formulated as a maximum likelihood estimation problem from incomplete data. We show that estimates of the model parameters can be computed through an iterative expectation-maximization algorithm and we describe a forward-backward procedure for its implementation. We report the results of a systematical evaluation of multigrams for language modeling on the ATIS database. The objective performance measure is the test set perplexity. Our results show that multigrams outperform conventional n-grams for this task.
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Deligne et al. (2002) studied this question.
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