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.
 This paper is a kind of follow-up on previous work such as Zhang et al (2017) trying to empirically analyze the relationship between model capacity and generalization for deep neural networks. In particular, this paper concentrates on sequence models for text classification, using LSTMs, CNNs or Bag-of-Words. They follow similar experiments as Zhang et al (2017) which was for images and obtain similar conclusions for text models. Given previous results on this topic, the results of this paper are not surprising: all results follow what previous papers have already shown, except that now it extends to recurrent and convolutional models for text. Overall, this is a nice report, but there is not enough novelty. Smaller points: - it would have been better to use state-of-the-art models for the selected task as a baseline, and then vary some design choices accordingly (like replacing the LSTM layer by a CNN layer, or Adam by SGD, etc. The current setting feels arbitrary as it is not clear that any of the models are good (with respect to the state-of-the-art). - I didn*t understand the argument trying to convince us that SGD performs implicit generalization; I agree it should be the case, but I didn*t understand the explanation, nor understood what was the "mode selection" and the "pattern of distribution".
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>



 This paper is a kind of follow-up on previous work such as Zhang et al (2017) trying to empirically analyze the relationship between model capacity and generalization for deep neural networks. In particular, this paper concentrates on sequence models for text classification, using LSTMs, CNNs or Bag-of-Words. They follow similar experiments as Zhang et al (2017) which was for images and obtain similar conclusions for text models. Given previous results on this topic, the results of this paper are not surprising: all results follow what previous papers have already shown, except that now it extends to recurrent and convolutional models for text. Overall, this is a nice report, but there is not enough novelty. Smaller points: - it would have been better to use state-of-the-art models for the selected task as a baseline, and then vary some design choices accordingly (like replacing the LSTM layer by a CNN layer, or Adam by SGD, etc. The current setting feels arbitrary as it is not clear that any of the models are good (with respect to the state-of-the-art). - I didn*t understand the argument trying to convince us that SGD performs implicit generalization; I agree it should be the case, but I didn*t understand the explanation, nor understood what was the "mode selection" and the "pattern of distribution".
assistant
 This paper is a kind of follow-up on previous work such as Zhang et al (2017) trying to empirically analyze the relationship between model capacity and generalization for deep neural networks. In particular, this paper concentrates on sequence models for text classification, using LSTMs, CNNs or Bag-of-Words. They follow similar experiments as Zhang et al (2017) which was for images and obtain similar conclusions for text models. Given previous results on this topic, the results of this paper are not surprising: all results follow what previous papers have already shown, except that now it extends to recurrent and convolutional models for text. Overall, this is a nice report, but there is not enough novelty. Smaller points: - it would have been better to use state-of-the-art models for the selected task as a baseline, and then vary some design choices accordingly (like replacing the LSTM layer by a CNN layer, or Adam by SGD, etc. The current setting feels arbitrary as it is not clear that any of the models are good (with respect to the state-of-the-art). - I didn*t understand the argument trying to convince us that SGD performs implicit generalization; I agree it should be the case, but I didn*t understand the explanation, nor understood what was the "mode selection" and the "pattern of distribution".