“Exploring the Limits of Language Modeling”, 2016-02-07 (; similar):
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 and Long-Short Term Memory, on the One Billion Word Benchmark.
Our best single model 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.