Reviews: Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

This is an important goal, because humans have neural networks that can make sense of language, and there may be a lot that the machine learning community can learn from understanding these exemplary language processors better. Indeed, a teleological explanation of how human or artificial neural-networks process language would be an enormous breakthrough in language science. The reviewers are positive about this paper and therefore I support them in recommending acceptance. To add to the positive aspects that they point out, I have a few concerns about the framing, which I list below in case they can improve the camera ready submission: 1) The abstract states that: "it is still unclear what the representations learned by these networks correspond to". It seems to me that this paper does not really answer the question it poses in the first line of the abstract.