Joint Reasoning for Temporal and Causal Relations

Qiang Ning, Zhili Feng, Hao Wu, Dan Roth


Abstract
Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must occur earlier than its effect, temporal and causal relations are closely related and one relation often dictates the value of the other. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.
Anthology ID:
P18-1212
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2278–2288
Language:
URL:
https://aclanthology.org/P18-1212
DOI:
10.18653/v1/P18-1212
Bibkey:
Cite (ACL):
Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. 2018. Joint Reasoning for Temporal and Causal Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2278–2288, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Joint Reasoning for Temporal and Causal Relations (Ning et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1212.pdf
Note:
 P18-1212.Notes.pdf
Presentation:
 P18-1212.Presentation.pdf
Video:
 https://vimeo.com/285805571
Data
TCR