Unsupervised Learning of Graph from Recipes
Aissatou Diallo Antonis Bikakis Luke Dickens Anthony Hunter Rob Miller
Cooking recipes are one of the most readily available kinds of procedural
text. They consist of natural language instructions that can be challenging to
interpret. In this paper, we propose a model to identify relevant information
from recipes and generate a graph to represent the sequence of actions in the
recipe. In contrast with other approaches, we use an unsupervised approach. We
iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$
encoding the texts (text-to-graph) one sequence at a time while providing the
supervision by decoding the graph into text (graph-to-text) and comparing the
generated text to the input. We evaluate the approach by comparing the
identified entities with annotated datasets, comparing the difference between
the input and output texts, and comparing our generated graphs with those
generated by state of the art methods.