  Understanding food recipe requires anticipating the implicit causal effects
of cooking actions, such that the recipe can be converted into a graph
describing the temporal workflow of the recipe. This is a non-trivial task that
involves common-sense reasoning. However, existing efforts rely on hand-crafted
features to extract the workflow graph from recipes due to the lack of
large-scale labeled datasets. Moreover, they fail to utilize the cooking
images, which constitute an important part of food recipes. In this paper, we
build MM-ReS, the first large-scale dataset for cooking workflow construction,
consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps
are multi-modal, featuring both text instructions and cooking images. We then
propose a neural encoder-decoder model that utilizes both visual and textual
information to construct the cooking workflow, which achieved over 20%
performance gain over existing hand-crafted baselines.
