NGramMarginal

Description

(Available in versions 1.1.0 and higher.)

This operation re-estimates smoothed n-gram models by imposing marginalization constraints similar to those used for Kneser-Ney modeling on Absolute Discounting models. Specifically, the algorithm modifies lower-order distributions so that the expected frequencies of lower-order n-grams within the model are equal to the smoothed relative frequency estimates of the baseline smoothing method. Unlike Kneser-Ney, this algorithm may require multiple iterations to converge, due to changes in the state probabilities.

Usage

ngrammarginalize [--opts] [in.mod [out.mod]]
  --iterations: type = int, default = 1, number of iterations of steady state probability calculation
  --max_bo_updates: type = int, default = 10, maximum within iteration updates to backoff weights
  --output_each_iteration: type = bool, default = false, whether to output a model after each iteration in addition to final model
  --steady_state_file: type = string, default = "", name of separate file to derive steady state probabilities
 
 class NGramMarginal(StdMutableFst *model);
 

Examples

ngrammarginalize --iterations=5 earnest.mod >earnest.marg.mod


int total_iterations = 5;
vector<double> weights;
for (int iteration = 1; iteration <= total_iterations; ++iteration) {
  StdMutableFst *model = StdMutableFst::Read("in.mod", true);
  NGramMarginal ngrammarg(model);
  ngrammarg.MarginalizeNGramModel(&weights, iteration, total_iterations);
  if (iteration == total_iterations)
    ngrammarg.GetFst().Write("out.mod");
  delete model;
}

Caveats

Note that this method assumes that the baseline smoothed model provides smoothed relative frequency estimates for all n-grams in the model. Thus the method is not generally applicable to models trained using Kneser-Ney smoothing, since lower-order n-gram weights resulting from that method do not represent relative frequency estimates. See reference below for further information on the algorithm.

References

B. Roark, C. Allauzen and M. Riley. 2013. "Smoothed marginal distribution constraints for language modeling". In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 43-52. The BibTex entry is here.
Topic revision: r3 - 2013-08-07 - BrianRoark
 
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