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 authors describe a method for encoding text into a discrete representation / latent space. On a measure that they propose, they should it outperforms an alternative Gumbel-Softmax method for both language modeling and NMT. The proposed method seems effective, and the proposed DSAE metric is nice, though it’s surprising if previous papers have not used metrics similar to normalized reduction in log-ppl. The datasets considered in the experiments are also large, another plus. However, overall, the paper is difficult to read and parse, especially since low-level details are weaved together with higher-level points throughout, and are often not motivated. The major critique would be the qualitative nature of results in the sections on “Decipering the latent code” and (to a lesser extent) “Mixed sample-beam decoding.” These two sections are simply too anecdotal, although it is nice being stepped through the reasoning for the single example considered in Section 3.3. Some quantitative or aggregate results are needed, and it should at least be straightforward to do so using human evaluation for a subset of examples for diverse decoding.
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 authors describe a method for encoding text into a discrete representation / latent space. On a measure that they propose, they should it outperforms an alternative Gumbel-Softmax method for both language modeling and NMT. The proposed method seems effective, and the proposed DSAE metric is nice, though it’s surprising if previous papers have not used metrics similar to normalized reduction in log-ppl. The datasets considered in the experiments are also large, another plus. However, overall, the paper is difficult to read and parse, especially since low-level details are weaved together with higher-level points throughout, and are often not motivated. The major critique would be the qualitative nature of results in the sections on “Decipering the latent code” and (to a lesser extent) “Mixed sample-beam decoding.” These two sections are simply too anecdotal, although it is nice being stepped through the reasoning for the single example considered in Section 3.3. Some quantitative or aggregate results are needed, and it should at least be straightforward to do so using human evaluation for a subset of examples for diverse decoding.
assistant
 The authors describe a method for encoding text into a discrete representation / latent space. On a measure that they propose, they should it outperforms an alternative Gumbel-Softmax method for both language modeling and NMT. The proposed method seems effective, and the proposed DSAE metric is nice, though it’s surprising if previous papers have not used metrics similar to normalized reduction in log-ppl. The datasets considered in the experiments are also large, another plus. However, overall, the paper is difficult to read and parse, especially since low-level details are weaved together with higher-level points throughout, and are often not motivated. The major critique would be the qualitative nature of results in the sections on “Decipering the latent code” and (to a lesser extent) “Mixed sample-beam decoding.” These two sections are simply too anecdotal, although it is nice being stepped through the reasoning for the single example considered in Section 3.3. Some quantitative or aggregate results are needed, and it should at least be straightforward to do so using human evaluation for a subset of examples for diverse decoding.