Review for NeurIPS paper: Faithful Embeddings for Knowledge Base Queries

When vacuous sketches are used in the intermediate steps, e.g. in R1 in MetaQA model, what is the intermediate output? Is it the dense-sparse representation of the entities in top-k facts? Isn't that a problem when k is large? Won't this be an issue in case there is a template that requires intersection as well in addition to unions? 3. For a given query, EmQL ranks all the entities (or gives a distribution over entities) instead of explicitly giving a set as an answer.