Abstract
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals into hierarchical script generation. Furthermore, We also design and evaluate the model to discover subgoal, and find that it is a bit more difficult to decompose the goals than summarizing from segmented steps.- Anthology ID:
- 2023.findings-acl.644
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10129–10147
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.644
- DOI:
- Cite (ACL):
- Xinze Li, Yixin Cao, Muhao Chen, and Aixin Sun. 2023. Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10129–10147, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation (Li et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.findings-acl.644.pdf