Abstract
Accurately interpreting the relationships between actions in a recipe text is essential to successful recipe completion. We explore using Abstract Meaning Representation (AMR) to represent recipe instructions, abstracting away from syntax and sentence structure that may order recipe actions in arbitrary ways. We present an algorithm to split sentence-level AMRs into action-level AMRs for individual cooking steps. Our approach provides an automatic way to derive fine-grained AMR representations of actions in cooking recipes and can be a useful tool for downstream, instructional tasks.- Anthology ID:
- 2023.dmr-1.6
- Volume:
- Proceedings of the Fourth International Workshop on Designing Meaning Representations
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Julia Bonn, Nianwen Xue
- Venues:
- DMR | WS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–67
- Language:
- URL:
- https://aclanthology.org/2023.dmr-1.6
- DOI:
- Cite (ACL):
- Katharina Stein, Lucia Donatelli, and Alexander Koller. 2023. From Sentence to Action: Splitting AMR Graphs for Recipe Instructions. In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 52–67, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- From Sentence to Action: Splitting AMR Graphs for Recipe Instructions (Stein et al., DMR-WS 2023)
- PDF:
- https://preview.aclanthology.org/ijclclp-past-ingestion/2023.dmr-1.6.pdf