Abstract
When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.- Anthology ID:
- 2020.emnlp-main.612
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7583–7596
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.612
- DOI:
- 10.18653/v1/2020.emnlp-main.612
- Cite (ACL):
- Noah Weber, Rachel Rudinger, and Benjamin Van Durme. 2020. Causal Inference of Script Knowledge. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7583–7596, Online. Association for Computational Linguistics.
- Cite (Informal):
- Causal Inference of Script Knowledge (Weber et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.emnlp-main.612.pdf