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Decoding the core of procedural texts, exemplified by cooking recipes, iscrucial for intelligent reasoning and instruction automation. Procedural textscan be comprehensively defined as a sequential chain of steps to accomplish atask employing resources. From a cooking perspective, these instructions can beinterpreted as a series of modifications to a food preparation, which initiallycomprises a set of ingredients. These changes involve transformations ofcomestible resources. For a model to effectively reason about cooking recipes,it must accurately discern and understand the inputs and outputs ofintermediate steps within the recipe. Aiming to address this, we present a newcorpus of cooking recipes enriched with descriptions of intermediate steps ofthe recipes that explicate the input and output for each step. We discuss thedata collection process, investigate and provide baseline models based on T5and GPT-3.5. This work presents a challenging task and insight into commonsensereasoning and procedural text generation.

PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes