Abstract
Representing texts as information about entities has long been deemed effective in event reasoning. We propose OpenPI2.0, an improved dataset for tracking entity states in procedural texts. OpenPI2.0 features not only canonicalized entities that facilitate evaluation, but also salience annotations including both manual labels and automatic predictions. Regarding entity salience, we provide a survey on annotation subjectivity, modeling feasibility, and downstream applications in tasks such as question answering and classical planning.
Abstract (translated)
将文本代表实体信息在事件推理中被视为有效的手段已经得到了长期的认可。我们提出了OpenPI2.0，一个改进的 dataset，用于跟踪实体状态在程序文本中。OpenPI2.0不仅包括用于规范化实体的机制，以方便评估，还包括实体重要性注释，包括手动标签和自动预测。在实体重要性方面，我们提供了一份关于注释主观性、建模可行性以及在问答和经典计划等任务中下游应用的研究。
URL
https://arxiv.org/abs/2305.14603
