The proposed pre-training methods can be extended to other types of procedural text beyond recipes by adapting the supervision signals and training objectives to suit the specific characteristics of the new domain. For scientific processes, the pre-training methods can focus on the sequential nature of experiments or procedures, where the order of steps is crucial for understanding the outcomes. By using similar techniques such as Permutation Classification, Embedding Regression, and Skip-Clip but tailored to the scientific domain, the model can learn to anticipate the effects of actions on entities or variables in a step-wise manner.
In the case of manufacturing guides, the pre-training methods can be adjusted to capture the assembly or production processes involved. By incorporating the order of operations, the model can learn to predict the next steps in a manufacturing process, understand the dependencies between different tasks, and anticipate the outcomes of specific actions on the final product. This approach would require creating domain-specific datasets and fine-tuning the pre-trained models on manufacturing-related text to improve performance in this domain.