Understanding stories with large-scale common sense
Name
Understanding-Stories-Commonsense.pdf
Description
Accepted version
Size
640.32 KB
Format
Adobe PDF
Checksum (MD5)
30c95b834358c08d51e44964bbc3e755
Author(s)
Williams, Bryan Michael
Lieberman, Henry A
Winston, Patrick H
Date Issued
2017
Journal
Proceedings of the Thirteenth International Symposium on Commonsense Reasoning
Publisher
CEUR-WS
Citation
Williams, Bryan et al. "Understanding stories with large-scale common sense." Proceedings of the Thirteenth International Symposium on Commonsense Reasoning, November 2017, London, United Kingdom, CEUR-WS, 2017 © 2017 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
Story understanding systems need to be able to perform commonsense reasoning, specifically regarding characters' goals and their associated actions. Some efforts have been made to form large-scale commonsense knowledge bases, but integrating that knowledge into story understanding systems remains a challenge. We have implemented the Aspire system, an application of large-scale commonsense knowledge to story understanding. Aspire extends Genesis, a rule-based story understanding system, with tens of thousands of goalrelated assertions from the commonsense semantic network ConceptNet. Aspire uses ConceptNet's knowledge to infer plausible implicit character goals and story causal connections at a scale unprecedented in the space of story understanding. Genesis's rule-based inference enables precise story analysis, while ConceptNet's relatively inexact but widely applicable knowledge provides a significant breadth of coverage difficult to achieve solely using rules. Genesis uses Aspire's inferences to answer questions about stories, and these answers were found to be plausible in a small study. Though we focus on Genesis and ConceptNet, demonstrating the value of supplementing precise reasoning systems with large-scale, scruffy commonsense knowledge is our primary contribution.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://dblp.org/db/conf/commonsense/index.html
