On the State of the Art of Evaluation in Neural Language Models
  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 code bases 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.
