The Role of Semantic Parsing in Understanding Procedural Text

TL;DR

This paper explores how symbolic semantic parsing, from deep semantic parsers like TRIPS, can enhance reasoning about entity states in procedural texts, improving understanding and evaluation metrics.

Contribution

It introduces PROPOLIS, a symbolic reasoning framework, and demonstrates how semantic parsing info can be integrated into neural models for better procedural reasoning.

Findings

Semantic knowledge improves procedural understanding in models.

New metrics clarify challenges in procedural reasoning.

Integration of symbolic and neural methods enhances performance.

Abstract

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.