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
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–58
- Language:
- URL:
- https://aclanthology.org/W17-2708
- DOI:
- 10.18653/v1/W17-2708
- 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., EventStory 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-2708.pdf