Abstract
Scripts capture commonsense knowledge about everyday activities and their participants. Script knowledge proved useful in a number of NLP tasks, such as referent prediction, discourse classification, and story generation. A crucial step for the exploitation of script knowledge is script parsing, the task of tagging a text with the events and participants from a certain activity. This task is challenging: it requires information both about the ways events and participants are usually uttered in surface language as well as the order in which they occur in the world. We show how to do accurate script parsing with a hierarchical sequence model and transfer learning. Our model improves the state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants.- Anthology ID:
- 2021.starsem-1.18
- Volume:
- Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Lun-Wei Ku, Vivi Nastase, Ivan Vulić
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 195–201
- Language:
- URL:
- https://aclanthology.org/2021.starsem-1.18
- DOI:
- 10.18653/v1/2021.starsem-1.18
- Cite (ACL):
- Fangzhou Zhai, Iza Škrjanec, and Alexander Koller. 2021. Script Parsing with Hierarchical Sequence Modelling. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 195–201, Online. Association for Computational Linguistics.
- Cite (Informal):
- Script Parsing with Hierarchical Sequence Modelling (Zhai et al., *SEM 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.starsem-1.18.pdf