In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a deep semantic parser~(TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into state-of-the-art neural models to conduct procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.

翻译：本文探究了从深层语义解析器中提取的符号语义表示是否有助于推理程序性文本中相关实体的状态。我们采用深层语义解析器（TRIPS）和语义角色标注作为两种语义解析知识来源。首先，我们提出了基于符号解析的程序性推理框架PROPOLIS。其次，我们将语义解析信息整合到当前最先进的神经模型中以进行程序性推理。实验表明，显式融入此类语义知识能提升程序性理解能力。本文提出了评估程序性推理任务的新指标，这些指标阐明了挑战所在，并揭示了神经模型、符号模型及集成模型之间的差异。