@unpublished{Jozefowicz2016,
abstract = {In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7. We also release these models for the NLP and ML community to study and improve upon.},
archivePrefix = {arXiv},
arxivId = {1602.02410},
author = {Jozefowicz, Rafal and Vinyals, Oriol and Schuster, Mike and Shazeer, Noam and Wu, Yonghui},
eprint = {1602.02410},
file = {:Users/shanest/Documents/Library/Jozefowicz et al/Unknown/Jozefowicz et al. - 2016 - Exploring the Limits of Language Modeling.pdf:pdf},
keywords = {model},
title = {{Exploring the Limits of Language Modeling}},
url = {https://arxiv.org/abs/1602.02410},
year = {2016}
}

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