Tree-Structured Variational Autoencoder
Many kinds of variable-sized data we would like to model contain an internal hierarchical structure in the form of a tree, including source code, formal logical statements, and natural language sentences with parse trees. For such data it is natural to consider a model with matching computational structure. In this work, we introduce a variational autoencoder-based generative model for tree-structured data. We evaluate our model on a synthetic dataset, and a dataset with applications to automated theorem proving. By learning a latent representation over trees, our model can achieve similar test log likelihood to a standard autoregressive decoder, but with the number of sequentially dependent computations proportional to the depth of the tree instead of the number of nodes in the tree.
The method overall seems to be a very interesting structural approach to variational autoencoders, however it seems to lack motivation as well as the application areas sufficient to prove its effectiveness. I see the attractiveness of using structural information in this context and I find it more intuitive than using a flat sequence representation, especially when there is a clear structure in the data. However experimental results seem to fail to be convincing in that regard. One issue is the lack of a variety of applications in general, the experiments seem to be very limited in that regard, considering that the paper itself speaks about natural language applications. It would be interesting to use the latent representations learned with the model for some other end task and see how much it impacts the success of that end task compared to various baselines. In my opinion, the paper has a potentially strong idea however in needs stronger results (and possibly in a wider variety of applications) as a proof of concept.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Tree-Structured Variational Autoencoder
Many kinds of variable-sized data we would like to model contain an internal hierarchical structure in the form of a tree, including source code, formal logical statements, and natural language sentences with parse trees. For such data it is natural to consider a model with matching computational structure. In this work, we introduce a variational autoencoder-based generative model for tree-structured data. We evaluate our model on a synthetic dataset, and a dataset with applications to automated theorem proving. By learning a latent representation over trees, our model can achieve similar test log likelihood to a standard autoregressive decoder, but with the number of sequentially dependent computations proportional to the depth of the tree instead of the number of nodes in the tree.
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 method overall seems to be a very interesting structural approach to variational autoencoders, however it seems to lack motivation as well as the application areas sufficient to prove its effectiveness. I see the attractiveness of using structural information in this context and I find it more intuitive than using a flat sequence representation, especially when there is a clear structure in the data. However experimental results seem to fail to be convincing in that regard. One issue is the lack of a variety of applications in general, the experiments seem to be very limited in that regard, considering that the paper itself speaks about natural language applications. It would be interesting to use the latent representations learned with the model for some other end task and see how much it impacts the success of that end task compared to various baselines. In my opinion, the paper has a potentially strong idea however in needs stronger results (and possibly in a wider variety of applications) as a proof of concept.
assistant
The method overall seems to be a very interesting structural approach to variational autoencoders, however it seems to lack motivation as well as the application areas sufficient to prove its effectiveness. I see the attractiveness of using structural information in this context and I find it more intuitive than using a flat sequence representation, especially when there is a clear structure in the data. However experimental results seem to fail to be convincing in that regard. One issue is the lack of a variety of applications in general, the experiments seem to be very limited in that regard, considering that the paper itself speaks about natural language applications. It would be interesting to use the latent representations learned with the model for some other end task and see how much it impacts the success of that end task compared to various baselines. In my opinion, the paper has a potentially strong idea however in needs stronger results (and possibly in a wider variety of applications) as a proof of concept.