Commonsense Knowledge Base Completion and Generation

Itsumi Saito, Kyosuke Nishida, Hisako Asano, Junji Tomita


Abstract
This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.
Anthology ID:
K18-1014
Volume:
Proceedings of the 22nd Conference on Computational Natural Language Learning
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–150
Language:
URL:
https://aclanthology.org/K18-1014
DOI:
10.18653/v1/K18-1014
Bibkey:
Cite (ACL):
Itsumi Saito, Kyosuke Nishida, Hisako Asano, and Junji Tomita. 2018. Commonsense Knowledge Base Completion and Generation. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 141–150, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Commonsense Knowledge Base Completion and Generation (Saito et al., CoNLL 2018)
Copy Citation:
PDF:
https://aclanthology.org/K18-1014.pdf
Data
ConceptNet