Title: Deep Generative Models with Learnable Knowledge Constraints,
Abstract: The broad set of deep generative models (DGMs) has achieved remarkable
advances. However, it is often difficult to incorporate rich structured domain
knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a
principled framework to impose structured constraints on probabilistic models,
but has limited applicability to the diverse DGMs that can lack a Bayesian
formulation or even explicit density evaluation. PR also requires constraints
to be fully specified a priori, which is impractical or suboptimal for complex
knowledge with learnable uncertain parts. In this paper, we establish
mathematical correspondence between PR and reinforcement learning (RL), and,
based on the connection, expand PR to learn constraints as the extrinsic reward
in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is
flexible to adapt arbitrary constraints with the model jointly. Experiments on
human image generation and templated sentence generation show models with
learned knowledge constraints by our algorithm greatly improve over base
generative models.