The Role of Semantic Parsing in Understanding Procedural Text
Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, James Allen
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.- Anthology ID:
- 2023.findings-eacl.137
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1837–1849
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.137
- DOI:
- 10.18653/v1/2023.findings-eacl.137
- Cite (ACL):
- Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, and James Allen. 2023. The Role of Semantic Parsing in Understanding Procedural Text. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1837–1849, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- The Role of Semantic Parsing in Understanding Procedural Text (Rajaby Faghihi et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-eacl.137.pdf