One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
Venue
ArXiv, Google (2013)
Publication Year
2013
Authors
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson
BibTeX
Abstract
We propose a new benchmark corpus to be used for measuring progress in statistical
language modeling. With almost one billion words of training data, we hope this
benchmark will be useful to quickly evaluate novel language modeling techniques,
and to compare their contribution when combined with other advanced techniques. We
show performance of several well-known types of language models, with the best
results achieved with a recurrent neural network based language model. The baseline
unpruned Kneser-Ney 5-gram model achieves perplexity 67.6; a combination of
techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy
(bits), over that baseline. The benchmark is available as a code.google.com project
at https://code.google.com/p/1-billion-word-language-modeling-benchmark/; besides
the scripts needed to rebuild the training/held-out data, it also makes available
log-probability values for each word in each of ten held-out data sets, for each of
the baseline n-gram models.
