A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
Topic modeling of text documents is one of the most important tasks in representation learning. In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders. On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically. On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner. Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks. Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.
 The paper proposes a VAE inference network for a non-parametric topic model. The model on page 4 is confusing to me since this is a topic model, so document-specific topic distributions are required, but what is shown is only stick-breaking for a mixture model. From what I can tell, the model itself is not new, only the fact that a VAE is used to approximate the posterior. In this case, if the model is nonparametric, then comparing with Wang, et al (2011) seems the most relevant non-deep approach. Given the factorization used in that paper, the q distributions are provably optimal by the standard method. Therefore, something must be gained by the VAE due to a non-factorized q. This would be best shown by comparing with the corresponding non-deep version of the model rather than LDA and other deep models.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
Topic modeling of text documents is one of the most important tasks in representation learning. In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders. On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically. On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner. Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks. Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.
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 a VAE inference network for a non-parametric topic model. The model on page 4 is confusing to me since this is a topic model, so document-specific topic distributions are required, but what is shown is only stick-breaking for a mixture model. From what I can tell, the model itself is not new, only the fact that a VAE is used to approximate the posterior. In this case, if the model is nonparametric, then comparing with Wang, et al (2011) seems the most relevant non-deep approach. Given the factorization used in that paper, the q distributions are provably optimal by the standard method. Therefore, something must be gained by the VAE due to a non-factorized q. This would be best shown by comparing with the corresponding non-deep version of the model rather than LDA and other deep models.
assistant
 The paper proposes a VAE inference network for a non-parametric topic model. The model on page 4 is confusing to me since this is a topic model, so document-specific topic distributions are required, but what is shown is only stick-breaking for a mixture model. From what I can tell, the model itself is not new, only the fact that a VAE is used to approximate the posterior. In this case, if the model is nonparametric, then comparing with Wang, et al (2011) seems the most relevant non-deep approach. Given the factorization used in that paper, the q distributions are provably optimal by the standard method. Therefore, something must be gained by the VAE due to a non-factorized q. This would be best shown by comparing with the corresponding non-deep version of the model rather than LDA and other deep models.