Beyond entity tracking, other aspects of procedural text understanding that could benefit from order-based pre-training strategies include entity identification, attribute extraction, and causal relationship modeling.
Entity Identification: Pre-training methods can be used to improve the model's ability to identify entities mentioned in procedural text accurately. By learning the sequential context of entities and their interactions, the model can better recognize and differentiate between different entities involved in a process.
Attribute Extraction: Understanding how attributes of entities change over the course of a procedure is essential for comprehensive procedural text understanding. Pre-training strategies that focus on capturing the evolution of entity attributes across steps can enhance the model's ability to extract and track these changes effectively.
Causal Relationship Modeling: By emphasizing the order of steps and the cause-effect relationships between actions and outcomes, pre-training methods can help the model infer causal connections within procedural text. This can enable the model to predict the consequences of specific actions, understand the reasons behind certain outcomes, and generate more accurate explanations of procedural processes.
By incorporating these additional aspects into the pre-training objectives and fine-tuning the models on relevant downstream tasks, the model can achieve a more holistic understanding of procedural text beyond entity tracking.