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 authors perform a comprehensive validation of LSTM-based word and character language models, establishing that recent claims that other structures can consistently outperform the older stacked LSTM architecture result from failure to fully explore the hyperparameter space. Instead, with more thorough hyperparameter search, LSTMs are found to achieve state-of-the-art results on many of these language modeling tasks.
This is a significant result in language modeling and a milestone in deep learning reproducibility research. The paper is clearly motivated and authoritative in its conclusions but it's somewhat lacking in detailed model or experiment descriptions.
Some further points:
- There are several hyperparameters set to the "standard" or "default" value, like Adam's beta parameter and the batch size/BPTT length. Even if it would be prohibitive to include them in the overall hyperparameter search, the community is curious about their effect and it would be interesting to hear if the authors' experience suggests that these choices are indeed reasonably well-justified.
- The description of the model is ambiguous on at least two points. First, it wasn't completely clear to me what the down-projection is (if it's simply projecting down from the LSTM hidden size to the embedding size, it wouldn't represent a hyperparameter the tuner can set, so I'm assuming it's separate and prior to the conventional output projection). Second, the phrase "additive skip connections combining outputs of all layers" has a couple possible interpretations (e.g., skip connections that jump from each layer to the last layer or (my assumption) skip connections between every pair of layers?).
- Fully evaluating the "claims of Collins et al. (2016), that capacities of various cells are very similar and their apparent
differences result from trainability and regularisation" would likely involve adding a fourth cell to the hyperparameter sweep, one whose design is more arbitrary and is neither the result of human nor machine optimization.
- The reformulation of the problem of deciding embedding and hidden sizes into one of allocating a fixed parameter budget towards the embedding and recurrent layers represents a significant conceptual step forward in understanding the causes of variation in model performance.
- The plot in Figure 2 is clear and persuasive, but for reproducibility purposes it would also be nice to see an example set of strong hyperparameters in a table. The history of hyperparameter proposals and their perplexities would also make for a fantastic dataset for exploring the structure of RNN hyperparameter spaces. For instance, it would be helpful for future work to know which hyperparameters' effects are most nearly independent of other hyperparameters.
- The choice between tied and clipped (Sak et al., 2014) LSTM gates, and their comparison to standard untied LSTM gates, is discussed only minimally, although it represents a significant difference between this paper and the most "standard" or "conventional" LSTM implementation (e.g., as provided in optimized GPU libraries). In addition to further discussion on this point, this result also suggests evaluating other recently proposed "minor changes" to the LSTM architecture such as multiplicative LSTM (Krause et al., 2016)
- It would also have been nice to see a comparison between the variational/recurrent dropout parameterization "in which there is further sharing of masks between gates" and the one with "independent noise for the gates," as described in the footnote. There has been some confusion in the literature as to which of these parameterizations is better or more standard; simply justifying the choice of parameterization a little more would also help.