Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper introduced ProbTransformer, which is a transformer based generative model. By adding a probabilistic architecture to the original transformer, ProbTransformer was able to model the hidden distribution of the data and provided probabilistic predictions. In addition, its contributions also include the online adaptation technique, kappa annealing, to gain better stability and performance. This paper is driven by the challenge of modeling the ambiguities of certain tasks, like the RNA folding problem. Deterministic models may harm the performance on those problems when one sample can have ambiguous outputs. The manuscript empirically demonstrates that the addition of their probabilistic layer improves not only the ability to model the latent data distribution but the stability of the learning process combined with the kappa annealing.

strengths_and_weaknesses: Strengths:
==========

The paper addresses an important question of modeling uncertainty in structure modeling. They show their modeling approach is applicable to different problems/domains.

The flow of this paper is clear. It introduces the motivation first and then its solution to the problem fits the motivation nicely. In addition, the experimental design supports their claims by showing their model outperforming the state-of-art models in different downstrain tasks. Specifically:

The authors nicely demonstrate improvements in the quality of distribution learning by using the ProbTransformer in the synthetic sequential dataset. 

To enhance the claim of distribution learning, the paper further shows that it can succeed in both RNA folding and molecule design problems, which use the encoder-only model and the decoder-only model to solve respectively. In both tasks, the authors provide some examples to illustrate their good performance while their evaluation metrics show that their model outperforms the other models in general. 

Using ablation studies, the authors demonstrate the effect and also the importance of the hierarchical probabilistic and kappa annealing design.


Weaknesses:
==============
The paper did not illustrate the model design very clearly. This is not a minor comment but an important one given what the authors are trying to achieve/claim here. The paper’s annotations and formulas give the audience a basic understanding of what the authors aim to do. However, it is hard to follow how the model is constructed. They should have either in the main text or supplementary a “walk through” example to clearly illustrate the model’s working. It is vague about the definition of encoder-only and decoder only models and how they are built differently in the downstream tasks. 

The authors mention that the encoder-decoder can not be built for now because of the same length issue, but it needs to be clarified why the model can’t make the same length for the output of the encoder with the input of the decoder. This relates to the previous comment as well.

The authors state RNA structure can be expressed by a dot-bracket notation, which means source and target can have the same length. However, do-bracket only captures nested dependencies and is not able to capture structures such as pseudo-knots. This is a key issue with the model which the authors sweep under the carpet. In contrast, other models are not limited by this (some none nested structures are shown in the predicted structures they included). Some discussion of these limitations compared to other methods is warranted, and evaluation would have been nice though possibly out of scope. 

Previous work also showed that RNA structure prediction methods greatly depend on how many canonical nested base pairing occurs - the fewer the ratio, the worse the model.  Again, at least mentioning/discussion of limitations is important.

To justify the model in the content of RNA the authors seem to mix a bit claims about mutations and molecules that shift between different structures in specific regions. It’s not clear to these reviewers the data actually support claims there. The authors should clarify/clean the text around these statements and make sure claims and results are well connected.

From the description in the main text it seems that the authors took a different approach compared to the authors of MXFold2 and SPOT RNA when defining train/test because they wanted to retain ambiguous structures. It is not clear if the comparisons done in the paper allowed these models to be trained in the same way. If not, there is the doubt that improvements are due at least to some extent to differences in training.

Minor comments:
=============
Line 21: “Vice-versa” is unclear in this context.
Line 169: insights in the 
Line 174-175: “token…is a unique distribution” 
Lack of labels for different kappa values in Figure 4.

questions: see above
limitations: Yes
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper introduces a novel probabilistic layer into the transformer architecture that incorporates cVAE, which allows multiple samples and predictions that as an ensemble may correspond to some distribution of interest. It also introduces a training strategy ("kappa annealing") to effectively train models with such layers while reducing need for hypers tuning.
strengths_and_weaknesses: While the elements of the main ideas are present, they are presented in a mathematically unclear manner with an unconvincing synthetic experiment (section 4.1) or experiments (section 4.2 and 4.3) whose significance is not well described for readers who are not familiar with the exact metrics used.

Examples of imprecise language and notations: 
- Abstract: "Therefore, we propose a hierarchical latent distribution to enhance one of the most successful deep learning models, the Transformer, to accommodate ambiguities and data distributions." What do the authors mean by "ambiguities and data distributions" mathematically and how do the chosen experiments address these?
- L76-75: "This objective at training time can be viewed as a reconstruction task, which is an easier task than prediction." Why is this easier?
- L99-101: "As a result of this hierar-chical composition of the ProbTransformer, the cVAE’s (conditional) prior model pθ (zm |Z<m , X )and generation model pρ(Y |X, Z) cannot be disentangled cleanly anymore in it and effectivelybecome a single model pΦ(Y |X)." What precisely do you mean by "disentangled cleanly anymore" and how does this relate to the training strategy?
- Eq4-6: what is i?
- Notations in Section 3.2 is under-defined and the material could be better presented for more effective communication. For example, show how the loss terms can be derived in a more principled manner from an optimization objective and variational approximation; what are p_m and q_m in Eq 9? A read could possibly infer from the context but it is on the authors to make this explicit.

Suggestions for experiments
- 4.1. I suggest to show some examples in the main text and how the tasks, objectives, and metrics are appropriate for quantifying the model's capability for "ambiguities and data distributions"; otherwise, readers can only guess what the authors mean. This synthetic example feels contrived and is not intuitive and convincing of how the proposed addition to the transformer architecture provides a meaningful advantage.

4.2. and 4.3. Similar to above, I suggest to show some examples in the main text and how the tasks, objectives, and metrics are appropriate for quantifying the model's capability for "ambiguities and data distributions"; otherwise, readers can only guess what the authors mean.


questions: See above.
limitations: Adequately addressed.
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: The authors present a probabilistic Transformer dubbed ProbTransfomer that models hierarchical latent distributions by integrating the transformer structure with a conditional variational auto-encoder. The article reveals that the incorporation of generalized ELBO with limited optimization is advantageous for variational training and that the proposed model excels at managing data ambiguities, including RNA folding and molecule design.

strengths_and_weaknesses: Strengths:
1.	The article is well organized and well written.
2.	Strong incentive exists where modeling unstructured molecules such as RNA is natural.
3.	Combining the Transformer with the cVAE, the authors propose a probabilistic layer for handling data ambiguity. ProbTransfomer's application to RNA folding and molecular design is a key development.
Weaknesses: 
1.	The innovation is marginal as main part of proposed method is simply combination of existing works (Transformer (Ashish et al., 2017), cVAE (Kihyuk et al., 2015), ELBO (Simon et al., 2019), and GECO (Danilo et al., 2018).) Some recent advances(e.g. Grphformer) are absence in related work and discussions.
2. The authors introduce the ProbTransfomer model in an effort to solve the challenge of RNA folding. However, the likelihood of alternative RNA structures arising are mostly dictated by biological circumstances, which the authors do not examine. In addition, authors should consider RNA abundance when building datasets.

questions: 1.	For the RNA folding problem, the authors may like to do a more comprehensive comparison of the ProbTransformer and other tools. For instance, independently evaluating the prediction of canonical and non-canonical base pairs.
2.	Considering adopting the ProbTransfomer model for predicting disordered protein areas. In contrast to the structural sections predicted by Alphafold2, disordered regions do not have fixed conformations and can therefore be seen as various target sequence conformational ensembles.
3.	Appendix line 754, the word “BRpRNA” should be changed to “BpRNA”.

limitations: N/A
review_title: null

Meta-review: metareview: This is a nice application-motivated paper that introduces and tests a novel stochastic variant of a transformer architecture.

All three reviewers recommend acceptance (albeit one is borderline). The borderline review focuses on the closeness of this to existing work, and the relatively incremental nature of the contribution. While I do see the potential concern, I think all in all the consensus is clearly to accept.

Interaction between reviewers and authors led to a number of beneficial changes to the paper during the review process.
confidence: Certain
Decision: Accept