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: In this paper, the authors study the compositional generalization ability, i.e. unseen feature combination of elementary combinations, of beta-(TC)VAE and emergent language with Gumbel-softmax training. Both of the methods have been argued for their ability to disentangle features in the representation. However, the results on the two image datasets the authors did experiments on showed that the latent representations learned by beta-(TC)VAE do not generalize well and existing metrics show a different trend than their proposed metric, while emergent language is better and allowing flexible length and stochastic sampling are essential to the better performance. 
strengths_and_weaknesses: Strengths:

1. It is interesting to see that despite being argued for strong disentanglement, beta-VAEs still cannot generalize well compositionally with the heuristic regularizations. Pointing out the discrepancy between these two metrics is essential for researchers to understand the capability of these latent variable models.
2. It is also interesting to see that using flexible length discrete channel works better than the continuous channel. 

Weaknesses:

I think the major weakness is the lack of discussion about evaluating compositional representation. Previous work, e.g. [1], has studied the latent structure of representations and methods to evaluate the compositional structure of them through simple compositional networks. I think this paper is in spirit similar to [1], and the difference and similarity should be addressed. 

[1] Measuring compositionality in representation learning. 
questions: Have you done any study on the relationship between message lengths of instances and instance novelty? It would be very interesting to see the characteristics of language use. For example, for more novel combinations (less maximum overlap with training instances) maybe longer sequences should be used to describe. 
limitations: Yes. The authors didn't discuss any negative societal impact, but I don't think there's any major negative societal impact in this paper that should be 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: **Main goal:** Come up with an evaluation methodology to evaluate compositional generalization (how consistently object attributes are expressed in representations obtained from test data) and use it to evaluate disentanglement and emergent language techniques to obtain insights about the representations that get learned. 

**Summary of the proposed evaluation pipeline for measuring compositional generalization:**
  1. Use the training set to learn representations in an unsupervised manner (using e.g. variation methods, or emergent language teqhniques)
  2. Using a small portion of the training data annotated with object attributes, train a simple (e.g. linear) probe to extract the attributes from the representations. 
  3. Check if the probe still works on unseen data. 

**Findings:**
* If one commits to a linear probe to measure compositional generalization, then using bottleneck representations doesn't yield the best generalization results - using the layer before and after work better. 
* Emergent language models yield better compositional generalization (when one uses a linear probe). 
* Compositional generalization doesn't correlate well with other metrics that quantify disentanglement. 


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**Post rebuttal update** I thank the authors for their response. I've increased my score.

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strengths_and_weaknesses: STRENGTHS
**Interesting research direction:** Trying to directly measure compositional generalization is interesting - usually this skill is presumed in downstream evaluations. Coming up with a robust way of measuring this would be quite valuable. 
**Useful signal for designing better techniques/algorithms:** As long as the proposed evaluation strategy is validated to genuinely capture compositional generalization, it can be a very useful tool in algorithm design by providing quantitative feedback. 

WEAKNESSES
**Limitations of linear separability as a measure of compositionality:** Linear separability of object attributes (i.e. the crux of the proposed evaluation scheme) might not be the best way to capture how "compositional" a given representation is. It is perhaps not surprising that the bottleneck representations perform worse at this: representations in higher dimensions are easier to separate. I understand that the proposed metric measures more than just separability by evaluating the learned probe on a held-out test set. However, since linear separability is a necessary condition for this type of probe to generalize in the first place, I fear the conclusions drawn from this evaluation methodology might be a bit misleading. 
**Affect of the probe model on results** This is a slightly general comment than the above one: how can we disentangle the inaccuracies caused by the probe itself form actual compositional generalization? This seems crucial to discuss here. 
  * (Sanity check experiment) For example, if one generated a dataset of perfectly disentangled representations (not necessarily linearly separable) and used the proposed evaluation pipeline, would the proposed metric identify these representations as having 'high compositionality'? Having a sanity check experiment like this to test the proposed evaluation metric seems important. 
  * (Another sanity check experiment) Another useful sanity check experiment could be checking how well the trained downstream probe works "in-distribution". That is, train the downstream probe with, say half the training data and evaluate it on the other half of the training data (not the test data). Does the probe generalize then? Are the results qualitatively different than if you evaluate on the unseen test set? 
**Narrow evaluation:** It'd be interesting to see if the current takeaways hold for other types of models and datasets. For example, how about the Slot Attention model (https://arxiv.org/abs/2006.15055), on the CLEVR dataset? 

questions: * Learning the linear probe from limited training labels: I don't quite understand why one would use a limited number of training examples so train the probe. Isn't higher == better, as long as we don't mix test data? 
* Line 52: Could you provide citations for the claim that today's SOTA unsupervised disentanglement models are built on top of variational generative models? 
* Could you discuss the results you obtain with gradient boosted trees a bit more? Do they agree with what you get with a linear probe? What are the main disagreements? 
limitations: A central limitation of the submission is that the proposed compositional generalization strategy isn't validation to fully capture compositional generalization. While high scores likely indicate strong compositional generalization, it's not clear whether low scores indicate lack of it. The paper would likely be stronger with sanity check experiments to validate the proposed eval methodology.
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 paper aims to investigate the compositional generalization performance of the representation learned from unsupervised learning, focusing on disentanglement and emergent language (EL). In contrast to previous works that measure compositional generalization on the learning task, the authors propose a two-stage evaluation protocol that does the same but on downstream tasks by introducing a simple header on top of the learned representation. Through experiments on two image tasks, they find that (1) representations from layers before or after the model bottleneck result in better compositional generalization (4.1); (2) previously proposed compositionality or disentanglement metrics do not always correlate with better performance (4.2); (3) EL appears to be more powerful than disentanglement models in such generalization (4.3). An ablation study follows to confirm the function of EL (e.g., hyperparameters).
strengths_and_weaknesses: Strengths:1). This paper focuses on the valuable problem of systematic generalization (i.e., generalization from learned concepts to unseen compositions) that plays a crucial role in improving the generalization of models and the following interpretation. 2). The work is well-motivated and focuses on compositional representation learning. The introduced evaluation protocol sounds reasonable and may be helpful for the followers to refer to. 3). The experimental results on two benchmarks are encouraging and supported by a further ablation study. The findings may benefit the usage of learned representations and distinguish the advance of EL over disentanglement models in compositional generalization.

Weaknesses:1). Technically speaking, the contribution of this work is incremental. As an analysis paper, its technique depth is shallow. 2). The reported experimental results appear to evidence the findings, while the encoder and decoder are fixed. More model structures. (e.g., transformers) should be taken into consideration to support the findings. 3). The work ends with the ablation study on EL; however, more studies and case analysis are necessary to underline the difference between the two learning methods (e.g., a failed case of disentanglement models but correctly inferred by EL).
questions: 1). In addition to the explored factors on lines 88-90, varying the model structure as the backbone of both encoder and decoder is needed.

2). Can EL predicts correctly for failed cases of disentanglement models, or the opposite?

3). Is there a case that leads to higher compositionality metrics but lower compositionality?

4). Other than dSprites and MPI3D-Real, I'm curious about whether the same findings can be observed from any application tasks.

limitations: I did not see a discussion regarding the limitations.
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: The authors study the compositional generalization performance of several approaches which aim to learn compositional representations including those focused on disentanglement and those based on emergent language. They propose a new metric to measure compositional generalization, specifically exploring novel combinations of previously-observed concepts. They find that, perhaps counterintuitively, often the features immediately before or after the latent variable perform better on generalization metrics. They evaluate on multiple instances of each model and also analyze the relationship between the generalization performance metrics and ones commonly used to evaluate disentanglement 
strengths_and_weaknesses: Strengths: 
- The results are non-obvious and have implications for other representation learning research which, for autoencoders, often (though not always) focuses evaluations on the latent representations.
- The experiments are thorough, covering a broad range of interesting questions, and well-grounded in disentanglement literature. In particular, this sheds some important light on questions left open by the widely-referenced Locatello et al. 2018., as discussed in Section 5.
- The metrics are reasonable, the datasets are standard for the disentanglement literature (though more would always be welcome), and the models are also reasonably chosen. 

Weaknesses:
- Given the paper’s title and claims, other kinds of representation-learning approaches would have been reasonable (e.g. encoder-only self-supervised techniques like SimCLR or BYOL, masked autoencoders, decoder-only ones like InfoGAN coupled with projection). However, the results carry useful implications even without this.
questions: “we find that the previously proposed metrics for evaluating the levels of compositionality are not correlated with actual compositional generalization in our framework” - Why might this be? The paragraph on Implications in Sec 4.2 discusses certain conclusions which cannot necessarily be drawn, but what does it imply if “compositionality exhibited in representations learning …  may not match with [the] human definition”? If they are different, how might they be different?
limitations: The limitations section in the Appendix is reasonable and complete.
review_title: null

Meta-review: metareview: This paper investigates compositional generalisation (CG) of the representations learned from unsupervised learning through the lens of disentanglement and emergent language. They argue that models that are primed to disentangle do not learn representations that do well on CG and that models of emergent language do indeed perform well on CG. They further explore utility of EL by finding improved performance on plain generalisation and in learning from fewer labels.

The reviewers agree that the paper tackles an interesting and relevant problem, and the question and derived insights are valuable, and the experiments are quite thorough.
Where the reviewers raised valid concerns about evaluation, these were addressed by the authors in their rebuttal along with clarification on separability vs CG and additional experiments.

The only concerns that I believe could still be argued for here are that questions about the quality of representations could extend to models such as SimCLR/BYOL/MAE and that it would strengthen the paper a good deal if these were also discussed compared against.
To tighten the implied brief of the paper, it might be useful to rejig the title a bit---it currently comes across as exploring a spectrum between disentanglement and EL, whereas it's likely simpler to just state 'EL shows better CG than disentanglement as an objective' or something similar.

Overall, I believe this is a good paper, and should be accepted.
confidence: Certain
Decision: Accept