Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting.
This paper applies the idea of the adversarial training and virtual adversarial training to the LSTM-based model in the text context. The paper is in general well written and easy to follow. Extending the idea of the adversarial training to the text tasks is simple but non-trivial. Overall the paper is worth to publish. I only have a minor comment: it is also interesting to see how much adversarial training can help in the performance of RNN, which is a simpler model and may be easier to analyze.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting.
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 applies the idea of the adversarial training and virtual adversarial training to the LSTM-based model in the text context. The paper is in general well written and easy to follow. Extending the idea of the adversarial training to the text tasks is simple but non-trivial. Overall the paper is worth to publish. I only have a minor comment: it is also interesting to see how much adversarial training can help in the performance of RNN, which is a simpler model and may be easier to analyze.
assistant
This paper applies the idea of the adversarial training and virtual adversarial training to the LSTM-based model in the text context. The paper is in general well written and easy to follow. Extending the idea of the adversarial training to the text tasks is simple but non-trivial. Overall the paper is worth to publish. I only have a minor comment: it is also interesting to see how much adversarial training can help in the performance of RNN, which is a simpler model and may be easier to analyze.