Syntax-Directed Variational Autoencoder for Structured Data (1802.08786v1)

Abstract: 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 the 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 introduces an SD-VAE that enforces both syntax and semantic constraints during generation to ensure valid discrete structures.
• It employs stochastic lazy attributes within a structured encoder-decoder framework, achieving near-perfect reconstruction in programming and molecular data.
• The model’s latent space significantly enhances Bayesian optimization and predictive performance, outperforming traditional CVAE and GVAE methods.

Overview of Syntax-Directed Variational Autoencoder for Structured Data

The paper "Syntax-Directed Variational Autoencoder for Structured Data" presents an innovative approach to addressing the challenges of generating syntactically and semantically correct structured data within deep generative models. While deep generative models like VAEs and GANs have been successful in modeling continuous data, the generation of valid discrete structures—such as computer programs and molecular structures—remains an intricate task due to the inherent complexity of their syntax and semantics.

Key Contributions

This paper introduces the Syntax-Directed Variational Autoencoder (SD-VAE), employing stochastic lazy attributes to address the limitations of existing variational autoencoders when handling discrete data structures. Unlike the prior methods which often result in semantically invalid structures, SD-VAE incorporates both syntax and semantic constraints directly into the generative process. The method takes inspiration from syntax-directed translation (SDT) used in compiler theory.

Model Architecture

1. Stochastic Syntax-Directed Decoder:
   • The decoder utilizes stochastic lazy attributes that integrate checkpointing constraints into the generation process.
   • By leveraging these attributes, it converts the typically offline semantic checks into active constraints that guide the output during decoding, ensuring validity and correctness.
2. Structure-Based Encoder:
   • The encoder was designed to capture structured inputs by compressing them into a continuous latent space while maintaining the structural integrity, facilitating better reconstruction and optimization.

Experimental Evaluation

The paper evaluates SD-VAE using two key applications: programming and molecular generation.

• Reconstruction Accuracy and Prior Validity: The SD-VAE demonstrates superior reconstruction accuracy and validity from prior samples, outperforming other methods such as Character VAE (CVAE) and Grammar VAE (GVAE). It achieves near-perfect reconstruction on programming data and a significant improvement in valid molecule reconstruction.
• Bayesian Optimization: Utilizing BO in experimental setups, SD-VAE finds programs and molecules with better properties, showcasing the efficacy of its latent space in understanding and optimizing complex structures.
• Predictive Performance: The SD-VAE's latent spaces are highly discriminative, evidenced by better log-likelihood and RMSE scores compared to traditional approaches, which is crucial for downstream tasks like property predictions in molecules.

Implications and Future Directions

The proposed SD-VAE not only shows considerable empirical advantages over conventional VAEs in handling structured data but also aligns with broader trends towards integrating formal methods within machine learning models. It can have impactful implications in drug discovery and automated code generation, where the generation of semantically correct structures is paramount.

Future research might focus on enhancing the versatility of SD-VAE to accommodate even more complex structured domains and exploring its applications beyond the typical benchmarks. Additionally, extending the theoretical framework of SD-VAE could enable new advancements in the field of AI, especially in creating systems capable of understanding and generating structurally intricate data autonomously.