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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Language Modelling | One Billion Word | PPL | 23.7 | 10 LSTM+CNN inputs + SNM10-SKIP (ensemble) |
| Language Modelling | One Billion Word | PPL | 30 | LSTM-8192-1024 + CNN Input |
| Language Modelling | One Billion Word | PPL | 30.6 | LSTM-8192-1024 |