Despite recent advancements, modern kitchens, at best, have one or more isolated (non-communicating) “smart” devices. The vision of having a fully-fledged am...

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RiCoRecA is a novel annotation schema for parsing cooking recipes into workflow representations suitable for IoT automation. The schema combines named entity recognition, relation classification, and coreference resolution into a single joint model. A dataset of 156 annotated recipes was created, and experiments comparing PEGASUS-X and LongT5 transformer models showed PEGASUS-X outperformed LongT5 by 39% in F-Score across all tasks, achieving near-human performance. The approach differs from previous work by handling conditional statements for sensor-based actions and learning multiple information extraction tasks jointly rather than through pipelines.

RiCoRecA: rich cooking recipe annotation schema

5 Generative encoder-decoder transformer model