Keywords: rnn, language modelling
TL;DR: Show that LSTMs are as good or better than recent innovations for LM and that model evaluation is often unreliable.
Abstract: Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.
Data: [Penn Treebank](https://paperswithcode.com/dataset/penn-treebank), [WikiText-2](https://paperswithcode.com/dataset/wikitext-2)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:1707.05589/code)