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 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.
 The submitted manuscript describes an exercise in performance comparison for neural language models under standardization of the hyperparameter tuning and model selection strategies and costs. This type of study is important to give perspective to non-standardized performance scores reported across separate publications, and indeed the results here are interesting as they favour relatively simpler structures. I have a favourable impression of this paper but would hope another reviewer is more familiar with the specific application domain than I am.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
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 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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The submitted manuscript describes an exercise in performance comparison for neural language models under standardization of the hyperparameter tuning and model selection strategies and costs. This type of study is important to give perspective to non-standardized performance scores reported across separate publications, and indeed the results here are interesting as they favour relatively simpler structures. I have a favourable impression of this paper but would hope another reviewer is more familiar with the specific application domain than I am.
assistant
 The submitted manuscript describes an exercise in performance comparison for neural language models under standardization of the hyperparameter tuning and model selection strategies and costs. This type of study is important to give perspective to non-standardized performance scores reported across separate publications, and indeed the results here are interesting as they favour relatively simpler structures. I have a favourable impression of this paper but would hope another reviewer is more familiar with the specific application domain than I am.