Causal Inference of Script Knowledge

Noah Weber, Rachel Rudinger, Benjamin Van Durme


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.612.pdf
Video:
 https://slideslive.com/38939303