@inproceedings{wang-etal-2019-unsupervised,
title = "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
author = "Wang, Shuohang and
Zhang, Sheng and
Shen, Yelong and
Liu, Xiaodong and
Liu, Jingjing and
Gao, Jianfeng and
Jiang, Jing",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1094",
doi = "10.18653/v1/N19-1094",
pages = "882--891",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
%A Wang, Shuohang
%A Zhang, Sheng
%A Shen, Yelong
%A Liu, Xiaodong
%A Liu, Jingjing
%A Gao, Jianfeng
%A Jiang, Jing
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F wang-etal-2019-unsupervised
%X 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.
%R 10.18653/v1/N19-1094
%U https://aclanthology.org/N19-1094
%U https://doi.org/10.18653/v1/N19-1094
%P 882-891
Markdown (Informal)
[Unsupervised Deep Structured Semantic Models for Commonsense Reasoning](https://aclanthology.org/N19-1094) (Wang et al., NAACL 2019)
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.