Putting it all together: language model combination
نویسنده
چکیده
In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n-grams, skipping, modified Kneser-Ney smoothing, and clustering. While all of these techniques have been studied separately, they have rarely been studied in combination. We find some significant interactions, especially with smoothing techniques. The combination of all techniques leads to up to a 45% perplexity reduction over a Katz smoothed trigram model with no count cutoffs, the highest such perplexity reduction reported.
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