program synthesis from input-output examples, which typically assumes that the number of input-output examples is

We would like to thank all three reviewers for their thoughtful comments. R3 saw our approach as "very similar to the standard approach for neural We believe our model actually differs significantly from previous approaches in this regard. While our code is able to perform a "double attention" mechanism, this work does not use these features of We thank R1 and apologize for this confusion. According to R2, our paper "shows quite convincingly that neural program Our revision will report this experiment and move the discussion on the heuristics to the main text. Our approach utilizes test-time search, which R3 also suggests is a disadvantage: "The results of [the no search In that sense, our approach offers more robustness than a neural-only model would allow. The reviewers note that our model uses strong supervision in the form of a meta-grammar. In a sense, we agree with R2: "Now that this paper has shown This demonstrates both generalization and graceful degradation on grammars with 3x the number of rules vs training.