论文标题
从教学手册中学习行动条件以了解教学
Learning Action Conditions from Instructional Manuals for Instruction Understanding
论文作者
论文摘要
推断动作前和后条件的能力对于理解复杂的指示至关重要，对于支持人类执行身体任务的应用程序，例如自主指导引导的代理和辅助AI等应用至关重要。在这项工作中，我们提出了一个称为动作条件推论的任务，并收集了教学手册中的前提条件和后条件的高质量注释的数据集。我们提出了一种弱监督的方法，以自动从在线教学手册中构建大规模培训实例，并策划密集的人类通知和经过验证的数据集，以研究当前的NLP模型如何在教学文本中推断动作条件依赖性。我们设计两种类型的模型因是否杠杆化和全球信息以及启发式方法的各种组合来构建薄弱的监督而有所不同。我们的实验结果表明，考虑到整个指导环境，F1得分的提高> 20％，并且通过提出的启发式方法> 6％的F1得分益处。
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.
