Abstract
Script knowledge is useful to a variety of NLP tasks. However, existing resources only cover a small number of activities, limiting its practical usefulness. In this work, we propose a zero-shot learning approach to script parsing, the task of tagging texts with scenario-specific event and participant types, which enables us to acquire script knowledge without domain-specific annotations. We (1) learn representations of potential event and participant mentions by promoting cluster consistency according to the annotated data; (2) perform clustering on the event / participant candidates from unannotated texts that belongs to an unseen scenario. The model achieves 68.1/74.4 average F1 for event / participant parsing, respectively, outperforming a previous CRF model that, in contrast, has access to scenario-specific supervision. We also evaluate the model by testing on a different corpus, where it achieved 55.5/54.0 average F1 for event / participant parsing.- Anthology ID:
- 2022.coling-1.356
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4049–4060
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.356
- DOI:
- Cite (ACL):
- Fangzhou Zhai, Vera Demberg, and Alexander Koller. 2022. Zero-shot Script Parsing. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4049–4060, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Zero-shot Script Parsing (Zhai et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.coling-1.356.pdf
- Data
- MCScript