Scalable Modified Kneser-Ney Language Model Estimation
Kenneth Heafield, Ivan Pouzyrevsky, Jonathan Clark, Mohammed Mediani and Philipp Koehn
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments show improvement of 0.63 to 1.01 BLEU point over constrained systems for the 2013 Workshop on Machine Translation task.
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