Trigger-based language models: a maximum entropy approach

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Abstract
Ongoing efforts at adaptive statistical language modeling are described. To extract information from the document history, trigger pairs are used as the basic information-bearing elements. To combine statistical evidence from multiple triggers, the principle of maximum entropy (ME) is used. To combine the trigger-based model with the static model, the latter is absorbed into the ME formalism. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. Among the advantages of this approach are its simplicity, generality, and incremental nature. Among its disadvantages are its computational requirements. The model described here was trained on five million words of Wall Street Journal text. It used some 40000 unigram constraints, 200000 bigram constraints, 200000 trigram constraints, and 60000 trigger constraints. After 13 iterations, it produced a language model whose perplexity was 12% lower than that of a conventional trigram, as measured on independent data.

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