Discrete Autoencoders for Sequence Models
Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current language models. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations.
 The topic is interesting however the description in the paper is lacking clarity. The paper is written in a procedural fashion - I first did that, then I did that and after that I did third. Having proper mathematical description and good diagrams of what you doing would have immensely helped. Another big issue is the lack of proper validation in Section 3.4. Even if you do not know what metric to use to objectively compare your approach versus baseline there are plenty of fields suffering from a similar problem yet doing subjective evaluations, such as listening tests in speech synthesis. Given that I see only one example I can not objectively know if your model produces examples like that *each* time so having just one example is as good as having none.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Discrete Autoencoders for Sequence Models
Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current language models. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations.
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 topic is interesting however the description in the paper is lacking clarity. The paper is written in a procedural fashion - I first did that, then I did that and after that I did third. Having proper mathematical description and good diagrams of what you doing would have immensely helped. Another big issue is the lack of proper validation in Section 3.4. Even if you do not know what metric to use to objectively compare your approach versus baseline there are plenty of fields suffering from a similar problem yet doing subjective evaluations, such as listening tests in speech synthesis. Given that I see only one example I can not objectively know if your model produces examples like that *each* time so having just one example is as good as having none.
assistant
 The topic is interesting however the description in the paper is lacking clarity. The paper is written in a procedural fashion - I first did that, then I did that and after that I did third. Having proper mathematical description and good diagrams of what you doing would have immensely helped. Another big issue is the lack of proper validation in Section 3.4. Even if you do not know what metric to use to objectively compare your approach versus baseline there are plenty of fields suffering from a similar problem yet doing subjective evaluations, such as listening tests in speech synthesis. Given that I see only one example I can not objectively know if your model produces examples like that *each* time so having just one example is as good as having none.