Our work is most similar to N2NMN (Hu et al., 2017) model, which learns both semantic operators and the layout in which to compose them.
optimizing the layouts requires reinforcement learning, which is challenging due to the high variance of policy gradients, whereas our chart-based approach is end-to-end differentiable.