Empirical Investigation on Model Capacity and Generalization of Neural Networks for Text
Recently, deep neural network models have shown promising opportunities for many natural language processing (NLP) tasks. In practice, the number of parameters of deep neural models is often significantly larger than the size of the training set, and its generalization behavior cannot be explained by the classic generalization theory. In this paper, with extensive experiments, we empirically investigate the model capacity and generalization of neural models for text. The experiments show that deep neural models can find patterns better than brute-force memorization. Therefore, a large-capacity model with early-stopping stochastic gradient descent (SGD) as implicit regularizer seems to be the best choice, as it has better generalization ability and higher convergence speed.
 Summary: As indicated by the paper*s title, this paper provides an "empirical investigation on model capacity and generalization of neural networks for text". It defines the capacity of a model to be the maximal number of random training points which a model can memorize (up to x% accuracy, for different values of x). It studies CNN, LSTM, and CBOW models, showing that larger models have larger capacity (the relationship between number of parameters , and the capacity of the model, appears to be ), and also generalize better. It also studies how the optimization method affects model capacity, showing that Adam is much more effective for memorizing random data than "vanilla SGD" or AdaGrad. Review: Quality: Experiments appear to be correct and of high quality. Clarity: The paper is relatively clear, though there are many grammatical mistakes. Also, the description of the output layer in section 3.3 didn*t make it clear to me that a single hidden layer neural network was used (I figured this out based on the number of parameters in the models) --- it made it seem that the output of the processing layer is directly connected to the softmax. Originality: The work is not very original, and the results are not surprising. Neyshabur et al (2014,2017) have already shown that larger networks generalize better than smaller networks, and Zhang et al (2017) have already shown that neural networks are able to both memorize and generalize. Significance: While the experiments presented in this paper are nice, I do not believe that the results are particularly significant or informative. Pros - Extensive experiments on NLP data. Cons - Results are not surprising - The questions tackled by the experiments are not original.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Empirical Investigation on Model Capacity and Generalization of Neural Networks for Text
Recently, deep neural network models have shown promising opportunities for many natural language processing (NLP) tasks. In practice, the number of parameters of deep neural models is often significantly larger than the size of the training set, and its generalization behavior cannot be explained by the classic generalization theory. In this paper, with extensive experiments, we empirically investigate the model capacity and generalization of neural models for text. The experiments show that deep neural models can find patterns better than brute-force memorization. Therefore, a large-capacity model with early-stopping stochastic gradient descent (SGD) as implicit regularizer seems to be the best choice, as it has better generalization ability and higher convergence speed.
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>



 Summary: As indicated by the paper*s title, this paper provides an "empirical investigation on model capacity and generalization of neural networks for text". It defines the capacity of a model to be the maximal number of random training points which a model can memorize (up to x% accuracy, for different values of x). It studies CNN, LSTM, and CBOW models, showing that larger models have larger capacity (the relationship between number of parameters , and the capacity of the model, appears to be ), and also generalize better. It also studies how the optimization method affects model capacity, showing that Adam is much more effective for memorizing random data than "vanilla SGD" or AdaGrad. Review: Quality: Experiments appear to be correct and of high quality. Clarity: The paper is relatively clear, though there are many grammatical mistakes. Also, the description of the output layer in section 3.3 didn*t make it clear to me that a single hidden layer neural network was used (I figured this out based on the number of parameters in the models) --- it made it seem that the output of the processing layer is directly connected to the softmax. Originality: The work is not very original, and the results are not surprising. Neyshabur et al (2014,2017) have already shown that larger networks generalize better than smaller networks, and Zhang et al (2017) have already shown that neural networks are able to both memorize and generalize. Significance: While the experiments presented in this paper are nice, I do not believe that the results are particularly significant or informative. Pros - Extensive experiments on NLP data. Cons - Results are not surprising - The questions tackled by the experiments are not original.
assistant
 Summary: As indicated by the paper*s title, this paper provides an "empirical investigation on model capacity and generalization of neural networks for text". It defines the capacity of a model to be the maximal number of random training points which a model can memorize (up to x% accuracy, for different values of x). It studies CNN, LSTM, and CBOW models, showing that larger models have larger capacity (the relationship between number of parameters , and the capacity of the model, appears to be ), and also generalize better. It also studies how the optimization method affects model capacity, showing that Adam is much more effective for memorizing random data than "vanilla SGD" or AdaGrad. Review: Quality: Experiments appear to be correct and of high quality. Clarity: The paper is relatively clear, though there are many grammatical mistakes. Also, the description of the output layer in section 3.3 didn*t make it clear to me that a single hidden layer neural network was used (I figured this out based on the number of parameters in the models) --- it made it seem that the output of the processing layer is directly connected to the softmax. Originality: The work is not very original, and the results are not surprising. Neyshabur et al (2014,2017) have already shown that larger networks generalize better than smaller networks, and Zhang et al (2017) have already shown that neural networks are able to both memorize and generalize. Significance: While the experiments presented in this paper are nice, I do not believe that the results are particularly significant or informative. Pros - Extensive experiments on NLP data. Cons - Results are not surprising - The questions tackled by the experiments are not original.