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.

本研究評估符號語義表示是否可以用來幫助程序文本中涉及的實體狀態推理，提出了基於符號解析的程序推理框架PROPOLIS，並將語義解析信息集成到最先進的神經模型中進行程序推理，在實驗中發現明確地合併此類語義知識可以提高程序理解，並提供了用於評估程序推理任務的新指標，明確了神經模型、符號模型和集成模型之間的差異。

语义分析在理解过程文本中的作用