Reviews: Compositional generalization through meta sequence-to-sequence learning

If the nuances aren't appropriately fleshed out, then the argument unfortunately reads as a strawman that the subsequent work sets aflame. Similarly, in the results and discussion the authors are encouraged to write a bit more to contextualize what is happening: through meta-learning, the models are learning *the fact that* primitives that are seen only once/a few times should be treated the same way as others, which is impossible to learn without meta-learning. Again, this is also a potential explanation for how and why humans behave this way; they have a ton of experience learning such facts. The implications of these results should be looped back into the claims made from previous work, such as the paper that introduced the SCAN dataset. Finally, I think that in their argumentation the authors need to keep in mind that they are demonstrating that when a neural network is given an opportunity to learn X, then it will most likely learn X. This is a bit obvious, but too many within this debate lose sight of this fact while trying to argue that neural networks as a class of model are inherently deficient. The results presented here speak directly against this notion and deserve to be emphasized.