Inference of Fine-Grained Event Causality from Blogs and Films

Zhichao Hu, Elahe Rahimtoroghi, Marilyn Walker


Abstract
Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
Anthology ID:
W17-2708
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
52–58
Language:
URL:
https://aclanthology.org/W17-2708
DOI:
10.18653/v1/W17-2708
Bibkey:
Cite (ACL):
Zhichao Hu, Elahe Rahimtoroghi, and Marilyn Walker. 2017. Inference of Fine-Grained Event Causality from Blogs and Films. In Proceedings of the Events and Stories in the News Workshop, pages 52–58, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Inference of Fine-Grained Event Causality from Blogs and Films (Hu et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2708.pdf