Multiple-Attribute Text Rewriting
The dominant approach to unsupervised "style transfer** in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style**. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.
 The paper proposes "style transfer" approaches for text rewriting that allow for controllable attributes. For example, given one piece of text (and the conditional attributes associated with the user who generated it, such as their age and gender), these attributes can be changed so as to generate equivalent text in a different style. This is an interesting application, and somewhat different from "style transfer" approaches that I*ve seen elsewhere. That being said I*m not particularly expert in the use of such techniques for text data. The architectural details provided in the paper are quite thin. Other than the starting point, which as I understand adapts machine translation techniques based on denoising autoencoders, the modifications used to apply the technique to the specific datasets used here were hard to follow: basically just a few sentences described at a high level. Maybe to somebody more familiar with these techniques will understand these modifications fully, but to me it was hard to follow whether something methodologically significant had been added to the model, or whether the technique was just a few straightforward modifications to an existing method to adapt it to the task. I*ll defer to others for comments on this aspect. Other than that the example results shown are quite compelling (both qualitatively and quantitatively), and the experiments are fairly detailed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Multiple-Attribute Text Rewriting
The dominant approach to unsupervised "style transfer** in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style**. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.
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 paper proposes "style transfer" approaches for text rewriting that allow for controllable attributes. For example, given one piece of text (and the conditional attributes associated with the user who generated it, such as their age and gender), these attributes can be changed so as to generate equivalent text in a different style. This is an interesting application, and somewhat different from "style transfer" approaches that I*ve seen elsewhere. That being said I*m not particularly expert in the use of such techniques for text data. The architectural details provided in the paper are quite thin. Other than the starting point, which as I understand adapts machine translation techniques based on denoising autoencoders, the modifications used to apply the technique to the specific datasets used here were hard to follow: basically just a few sentences described at a high level. Maybe to somebody more familiar with these techniques will understand these modifications fully, but to me it was hard to follow whether something methodologically significant had been added to the model, or whether the technique was just a few straightforward modifications to an existing method to adapt it to the task. I*ll defer to others for comments on this aspect. Other than that the example results shown are quite compelling (both qualitatively and quantitatively), and the experiments are fairly detailed.
assistant
 The paper proposes "style transfer" approaches for text rewriting that allow for controllable attributes. For example, given one piece of text (and the conditional attributes associated with the user who generated it, such as their age and gender), these attributes can be changed so as to generate equivalent text in a different style. This is an interesting application, and somewhat different from "style transfer" approaches that I*ve seen elsewhere. That being said I*m not particularly expert in the use of such techniques for text data. The architectural details provided in the paper are quite thin. Other than the starting point, which as I understand adapts machine translation techniques based on denoising autoencoders, the modifications used to apply the technique to the specific datasets used here were hard to follow: basically just a few sentences described at a high level. Maybe to somebody more familiar with these techniques will understand these modifications fully, but to me it was hard to follow whether something methodologically significant had been added to the model, or whether the technique was just a few straightforward modifications to an existing method to adapt it to the task. I*ll defer to others for comments on this aspect. Other than that the example results shown are quite compelling (both qualitatively and quantitatively), and the experiments are fairly detailed.