  Food is significant to human daily life. In this paper, we are interested in
learning structural representations for lengthy recipes, that can benefit the
recipe generation and food cross-modal retrieval tasks. Different from the
common vision-language data, here the food images contain mixed ingredients and
target recipes are lengthy paragraphs, where we do not have annotations on
structure information. To address the above limitations, we propose a novel
method to unsupervisedly learn the sentence-level tree structures for the
cooking recipes. Our approach brings together several novel ideas in a
systematic framework: (1) exploiting an unsupervised learning approach to
obtain the sentence-level tree structure labels before training; (2) generating
trees of target recipes from images with the supervision of tree structure
labels learned from (1); and (3) integrating the learned tree structures into
the recipe generation and food cross-modal retrieval procedure. Our proposed
model can produce good-quality sentence-level tree structures and coherent
recipes. We achieve the state-of-the-art recipe generation and food cross-modal
retrieval performance on the benchmark Recipe1M dataset.
