Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang


Abstract
Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.
Anthology ID:
N19-1094
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
882–891
Language:
URL:
https://aclanthology.org/N19-1094
DOI:
10.18653/v1/N19-1094
Bibkey:
Cite (ACL):
Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, and Jing Jiang. 2019. Unsupervised Deep Structured Semantic Models for Commonsense Reasoning. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 882–891, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (Wang et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1094.pdf
Data
WSC