# RiCoRecA: rich cooking recipe annotation schema

## TL;DR

The paper introduces RiCoRecA, a new schema for parsing cooking recipes into workflows for smart kitchens, using a joint model that outperforms existing methods.

## Contribution

The novel contribution is a joint model for multiple information extraction tasks in recipe parsing, achieving near-human performance.

## Key findings

- PEGASUS-X outperformed LongT5 by 39% in F-Score across all annotation tasks.
- RiCoRecA enables joint modeling of named entities, relations, coreference resolution, and entity tracking in recipe parsing.

## Abstract

Despite recent advancements, modern kitchens, at best, have one or more isolated (non-communicating) “smart” devices. The vision of having a fully-fledged ambient kitchen where devices know what to do and when has yet to be realized. To address this, we present RiCoRecA, a novel schema for parsing cooking recipes into a workflow representation suitable for automation, a step toward that direction. Methodologically, the schema requires a number of information extraction tasks, i.e., annotating named entities, identifying relations between them, coreference resolution, and entity tracking. RiCoRecA differs from previously reported approaches in that it learns these different information extraction tasks using one joint model. We also provide a dataset containing annotations that follow this schema. Furthermore, we compared two transformer-based models for parsing recipes into workflows, namely, PEGASUS-X and LongT5. Our results demonstrate that PEGASUS-X surpassed LongT5 on all of the annotation tasks. Specifically, PEGASUS-X surpassed LongT5 by 39% in terms of F-Score when averaging the performance on all the tasks; it demonstrated almost human-like performance.