Rather than ratable aspects that are common across the entire domain (e.g., "ingredient", "cooking method"), we are interested in actionable information that is related and specific to the subject of the review.
in this paper, we propose a generative model that jointly identifies user-proposed refinements in instruction reviews at multiple granularities, and aligns them to the appropriate steps in the original instructions.