Review for NeurIPS paper: Compositional Generalization via Neural-Symbolic Stack Machines

The paper proposes a new method for compositional generalization in sequence-to-sequence tasks. The basic idea is to have a symbolic stack machine (capable of compositionally manipulating sequences) that is controlled by a neural network. The method gets perfect accuracy on an existing compositional generalization dataset, a small-scale English-French machine translation task, and a grammar parsing task. Pros: Novel architecture Attractive way of providing inductive bias without hardcoding too much knowledge The paper is well-written Strong experimental results in the domains considered Cons: The paper could do more by the way of providing insights about why the model works. The reviewers appreciated the clarifications provided in the author feedback.