  There has recently been growing interest in the automatic generation of
cooking recipes that satisfy some form of dietary restrictions, thanks in part
to the availability of online recipe data. Prior studies have used pre-trained
language models, or relied on small paired recipe data (e.g., a recipe paired
with a similar one that satisfies a dietary constraint). However, pre-trained
language models generate inconsistent or incoherent recipes, and paired
datasets are not available at scale. We address these deficiencies with
RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given
ingredient-level critiques. The model is trained for recipe completion to learn
semantic relationships within recipes. Our work's main innovation is our
unsupervised critiquing module that allows users to edit recipes by interacting
with the predicted ingredients; the system iteratively rewrites recipes to
satisfy users' feedback. Experiments on the Recipe1M recipe dataset show that
our model can more effectively edit recipes compared to strong
language-modeling baselines, creating recipes that satisfy user constraints and
are more correct, serendipitous, coherent, and relevant as measured by human
judges.
