Commonsense mining as knowledge base completion? A study on the impact of novelty

Stanislaw Jastrzębski, Dzmitry Bahdanau, Seyedarian Hosseini, Michael Noukhovitch, Yoshua Bengio, Jackie Cheung


Abstract
Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.
Anthology ID:
W18-1002
Volume:
Proceedings of the Workshop on Generalization in the Age of Deep Learning
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Yonatan Bisk, Omer Levy, Mark Yatskar
Venue:
Gen-Deep
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8–16
Language:
URL:
https://aclanthology.org/W18-1002
DOI:
10.18653/v1/W18-1002
Bibkey:
Cite (ACL):
Stanislaw Jastrzębski, Dzmitry Bahdanau, Seyedarian Hosseini, Michael Noukhovitch, Yoshua Bengio, and Jackie Cheung. 2018. Commonsense mining as knowledge base completion? A study on the impact of novelty. In Proceedings of the Workshop on Generalization in the Age of Deep Learning, pages 8–16, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Commonsense mining as knowledge base completion? A study on the impact of novelty (Jastrzębski et al., Gen-Deep 2018)
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PDF:
https://aclanthology.org/W18-1002.pdf