Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2019
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.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Minneapolis, MN, June 2-7
First Page
882
Last Page
891
Identifier
10.18653/v1/N19-1094
Publisher
ACL
City or Country
S
Citation
WANG, Shuohang; ZHANG, Sheng; SHEN, Yelong; LIU, Xiaodong; LIU, Jingjing; GAO, Jianfeng; and JIANG, Jing.
Unsupervised deep structured semantic models for commonsense reasoning. (2019). Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Minneapolis, MN, June 2-7. 882-891.
Available at: https://ink.library.smu.edu.sg/sis_research/4789
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/N19-1094
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons