Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors develop a deep-coupling generative process for deep topic modeling, where the key idea is to incorporate skip connections into the generation to alleviate the issue that higher layer representations exhibit similar patterns. The authors further formulate the sequence-like generation procedure as a Markov decision process, and develop a Policy Gradient based (PG-based) learning method. The authors show the benefit of PG-based training over vanilla training for topic modeling. 

In comparison with previous methods, the experimental results show the consistent and significant benefits on three datasets. The qualitative evaluation (i.e., looking into generated topics and topic embeddings) also support the authors claim (overcoming ``posterior collapse”).
strengths_and_weaknesses: Novelty/Quality/Contributions:

The novelty of this draft is not on the techniques used; Actually, the techniques used in this draft are all not new. Using skip connection is heavily motivated by [15, 20], while using Policy gradient is also motivated by the success of previous works like [25], [26] and [27]. 

The authors effectively used those techniques to solve one issue — higher layers of deep PTMs/NTMs exhibit similar patterns — observed by [15]. This is the major novelty and contribution of the paper.

The authors also conducted thorough experimental study to support their claims. The authors found the proposed methods do outperform the baseline methods in terms of point log-likelihood (especially for higher-layer representations), Perplexity, topic diversity, clustering purity/NMI, diversity of generated topics (especially for higher-layer) and visual separability on topic embeddings. The authors also show the consistent benefits obtained from policy gradient based training on all the three datasets. 

This work does seem to be a high-quality draft to me, though I do have some questions for the authors in the Questions section.


Clarity:

Overall, the draft is not difficult to follow, with some potentially small issues:
1. The relationship of the projection matrices of different layers are not realized in the Figure 1(c)

2. Line 177 and Equation (9) are not consistent

3. Do superscript l (line 105) and superscript (l) (line 100) indicate the same thing?


Significance:

Given good quantitative and qualitative performance, the proposed methods should be of sufficient interest to the topic modeling community. 

questions: 1. The clear performance gap on dc-etm-alpha (Equation 2) and dc-etm-beta (Equation 3) does seem to be big. The authors can shed some light on it.

2. Regarding PG-based vs non-PG-based -- Table one/two do support that PG-based training is helpful. However, more analysis/discussions could be helpful:

    2.1. What is the motivation to use PG-based training, besides observing the success in [25], [26] and [27].

    2.2 According to Figure 2, PG-training does not seem to benefit point log-likelihood, but in Table 1 and 2, PG-based models exhibit consistent benefits, can the authors discuss more on this "discrepancy".

    2.3 Can the authors show qualitative comparison (e.g., t-sne plot of topic embeddings) between dc-ETM-alpha, dc-ETM-beta and dc-ETM-beta (Policy).

3. Regarding hierarchical representation:

    3.1. The table two does show that the higher-layer representations of PGBN/WHAI/SawETM do not help with documentation clustering; Interestingly, the 1st layer hidden layer of PGBN/WHAI/SawETM does seem to outperform dc-ETM.

    3.2. Can the authors compare the 4th/3rd/2nd/1st layer visualization for SawETM and dc-ETM?
limitations: Do not see clear issues.

Post discussion: I incline to retain my positive rating.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper presents a hierarchical neural topic model (NTM) along with
a policy gradient approach for learning the parameters. The core of
the NTM extends existing work (Embedding Topic Model: ETM). Learning
occurs within a variational autoencoder framework. To do this
formulation, the hiearchical parameters are treated as the actions
that must be produced, with the corresponding expectations of the ELBO
as the reward function. The paper performs quantitative and
qualitative comparisons on four datasets, comparing against six
baselines. The paper reports gains in perplexity, topic diversity,
topic purity, and NMI.
strengths_and_weaknesses: UPDATE post discussion: I thank the authors for their clarifications and promises to revise accordingly. I have updated my main S/W review. I am also updating my score.

=================================

From the very beginning of this paper, there is a focus on "posterior
collapse." However, the paper does not adequately or properly define
posterior collapse. This is a major weakness of the paper, and would
require significant revisions throughout. Notably:

W1. How is posterior collapse defined? [Post discussion: thank you for addressing this. I strongly recommend putting the revised definition in the main paper.]

W2. How is posterior collapse being measured? [Post discussion: thank you for addressing this. I strongly recommend clearly tying this to the experimental setup in the main paper.]

W3. What experimental results demonstrate that posterior collapse, as
measured by point (2), has actually been mitigated? [Post discussion: Please add the revised table in the appendix to the main paper.]

Beyond that overarching weakness, this paper does not adequately
address other considerations, including broader take-aways:

W4. This paper presents dc-ETM, and two learning algorithms for
it. What aspects of this work prevent posterior collapse? Is it the
definition of dc-ETM, the policy gradient approach, or both? [Post discussion: thank you for addressing this]

W5. Fundamentally, why does policy gradient result in better results?
The two objectives (Eq. 11 and Eq. 16) are very similar, sharing the
same subloss computations. From what I can tell, a core difference is
the quadratic nature of the policy gradient objective. Is this what is
responsible for the improved results, or is there something else at
play?

One strength of this work is that it examines different ways of
decoding, notably through Eqns 2 and 3.

W6. Unfortunately, the presentation of the model(s) needs to be
improved. While some of this could be a spacing issue, e.g., around
equation 1), the corresponding prose is quite dense and more difficult
to get through than it should be. Further, the core factorization and
probabilistic identities that this paper relies (e.g., the
deconstruction in Eq. 4) on are not specified clearly. I recommend
significantly rewriting and streamlining the presentation of the model
and underlying justifications. [Post discussion: thank you for addressing this]

W7. Finally, I found the qualitative results to be unclear and
difficult to interpret. Figure 5 does not have enough explanation,
either in the figure itself, the caption, or the corresponding prose. 
[Post discussion: thank you for addressing this]
questions: Q1. Please provide a brief explanation of W1 that could be added to a
revised paper.

Q2. Please provide a brief explanation of W2 that could be added to a
revised paper.

Q3. Please address W3.

Q4. Please provide a brief explanation of W4 that could be added to a
revised paper.

Q5. Please provide a brief explanation of W5 that could be added to a
revised paper.

Q6. How many samples for the VAE objective (Eq. 11) or the policy
gradient objective (Eq. 16)?

limitations: No, see W1-W4.

Post discussion: sufficiently addressed
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: A new hierarchical neural topic model is proposed that addresses the posterior collapse that occurs at higher levels with previous hierarchical neural topic models. The resulting topic model achieves much higher quality topics at higher levels. The method can be extended to different types of neural topic models.
strengths_and_weaknesses: # originality
The work has a high degree of novelty. The augmented representation of the documents which allows to reduce the posterior collapse is very interesting.

# quality
The claims are in general well supported. Nevertheless, the experimental section could be more convincing. It is uncommon nowadays to compare topic models with regard to perplexity though. It has been shown that perplexity does not correlate well with human judgment. Usually, topic quality, a product of diversity and coherence is used instead. For hierarchical topic models, it is even possible to evaluate the different layers separately since the coherence and diversity could be much different depending on the layer. There is a recent attempt in that direction which could be helpful in comparing different hierarchies [1]. The claim that the resulting topics have higher quality is hard to support with only perplexity and diversity measures.

# clarity
The paper is well written and structured.

# significance
The method is likely to be used and further developed. It is extendable to different deep topic models and thus has general applicability. 


minor comments:
- Figure 1: a) says Deep PGM but the description says Deep PTM
- line 129 alleviate -> alleviates


[1] Nagda et al. "Hierarchical Topic Evaluation: Statistical vs. Neural Models" Bayesian Deep Learning Workshop at NeurIPS 2021.
questions: - How does the method perform in comparison to traditional deep topic models like nHDP?
- What does the comparison look like with respect to topic coherence?
- Why are the Table 2 results only performed on the first hidden layer representations? 
- Is there some way to evaluate the hierarchy level-wise to be able to make quantitative claims about the topic quality at higher levels?
limitations: limitations are not addressed
review_title: null

Meta-review: metareview: This paper proposes a new hierarchical neural topic model that alleviates the posterior collapse problem of previous models. The key idea is to incorporate skip connections into the generation to alleviate the issue that higher layer representations exhibit similar patterns. The sequence-like generation procedure is formulated as a Markov decision process and learned with a policy gradient method. 

Overall, most reviewers feel positively about this paper. Even though the specific techniques used are not novel (policy gradient, skip connections) their use to alleviate posterior collapse in higher layers of neural topic models seems novel. The experimental results are convincing, and the qualitative evaluation support the claim of overcoming "posterior collapse." The proposed method is potentially useful and is applicable to different types of neural topic models. Although there were some concerns in the original version (e.g. insufficient explanation on posterior collapse and why the proposed method addresses it; evaluation limited to perplexity) the authors' response addressed most of the concerns and added details in the updated version. Therefore, I recommend acceptance.
confidence: Less certain
Decision: Accept