The Role of Semantic Parsing in Understanding Procedural Text
This work addresses procedural reasoning for natural language processing, but it is incremental as it builds on existing semantic parsing and neural methods.
The paper tackles the problem of reasoning over entity states in procedural text by integrating symbolic semantic parsing knowledge into neural models, resulting in improved procedural understanding.
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.