Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.
 The paper presents an approach for improving variational autoencoders for structured data that provide an output that is both syntactically valid and semantically reasonable. The idea presented seems to have merit , however, I found the presentation lacking. Many sentences are poorly written making the paper hard to read, especially when not familiar with the presented methods. The experimental section could be organized better. I didn*t like that two types of experiment are now presented in parallel. Finally, the paper stops abruptly without any final discussion and/or conclusion.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.
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 presents an approach for improving variational autoencoders for structured data that provide an output that is both syntactically valid and semantically reasonable. The idea presented seems to have merit , however, I found the presentation lacking. Many sentences are poorly written making the paper hard to read, especially when not familiar with the presented methods. The experimental section could be organized better. I didn*t like that two types of experiment are now presented in parallel. Finally, the paper stops abruptly without any final discussion and/or conclusion.
assistant
 The paper presents an approach for improving variational autoencoders for structured data that provide an output that is both syntactically valid and semantically reasonable. The idea presented seems to have merit , however, I found the presentation lacking. Many sentences are poorly written making the paper hard to read, especially when not familiar with the presented methods. The experimental section could be organized better. I didn*t like that two types of experiment are now presented in parallel. Finally, the paper stops abruptly without any final discussion and/or conclusion.