The ability to infer pre- and postconditions of an action is vital for comprehending complex instructions, and is essential for applications such as autonomous instruction-guided agents and assistive AI that supports humans to perform physical tasks. In this work, we propose a task dubbed action condition inference, and collecting a high-quality, human annotated dataset of preconditions and postconditions of actions in instructional manuals. We propose a weakly supervised approach to automatically construct large-scale training instances from online instructional manuals, and curate a densely human-annotated and validated dataset to study how well the current NLP models can infer action-condition dependencies in the instruction texts. We design two types of models differ by whether contextualized and global information is leveraged, as well as various combinations of heuristics to construct the weak supervisions. Our experimental results show a >20% F1-score improvement with considering the entire instruction contexts and a >6% F1-score benefit with the proposed heuristics.

本研究旨在通过分析在线指南手册的数据集以构建模型，研究当下NLP模型在指令文本中推断动作条件依赖性的效果， 提出了弱监督方法用于自动构建大规模训练实例，在考虑整体指导上进行了改进，在采用了全局信息后， F1-score的提高达到了20％以上。

从指导手册中学习操作条件进行指令理解