@inproceedings{zhang-li-2022-ke,
title = "{KE}-{GCL}: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering",
author = "Zhang, Lihui and
Li, Ruifan",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.6",
doi = "10.18653/v1/2022.findings-emnlp.6",
pages = "76--87",
abstract = "Commonsense question answering (CQA) aims to choose the correct answers for commonsense questions. Most existing works focus on extracting and reasoning over external knowledge graphs (KG). However, the noise in KG prevents these models from learning effective representations. In this paper, we propose a Knowledge Enhanced Graph Contrastive Learning model (KE-GCL) by incorporating the contextual descriptions of entities and adopting a graph contrastive learning scheme. Specifically, for QA pairs we represent the knowledge from KG and contextual descriptions. Then, the representations of contextual descriptions as context nodes are inserted into KG, forming the knowledge-enhanced graphs. Moreover, we design a contrastive learning method on graphs. For knowledge-enhanced graphs, we build their augmented views with an adaptive sampling strategy. After that, we reason over graphs to update their representations by scattering edges and aggregating nodes. To further improve GCL, hard graph negatives are chosen based on incorrect answers. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our proposed KE-GCL, which outperforms previous methods consistently.",
}
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%0 Conference Proceedings
%T KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering
%A Zhang, Lihui
%A Li, Ruifan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-li-2022-ke
%X Commonsense question answering (CQA) aims to choose the correct answers for commonsense questions. Most existing works focus on extracting and reasoning over external knowledge graphs (KG). However, the noise in KG prevents these models from learning effective representations. In this paper, we propose a Knowledge Enhanced Graph Contrastive Learning model (KE-GCL) by incorporating the contextual descriptions of entities and adopting a graph contrastive learning scheme. Specifically, for QA pairs we represent the knowledge from KG and contextual descriptions. Then, the representations of contextual descriptions as context nodes are inserted into KG, forming the knowledge-enhanced graphs. Moreover, we design a contrastive learning method on graphs. For knowledge-enhanced graphs, we build their augmented views with an adaptive sampling strategy. After that, we reason over graphs to update their representations by scattering edges and aggregating nodes. To further improve GCL, hard graph negatives are chosen based on incorrect answers. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our proposed KE-GCL, which outperforms previous methods consistently.
%R 10.18653/v1/2022.findings-emnlp.6
%U https://aclanthology.org/2022.findings-emnlp.6
%U https://doi.org/10.18653/v1/2022.findings-emnlp.6
%P 76-87
Markdown (Informal)
[KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering](https://aclanthology.org/2022.findings-emnlp.6) (Zhang & Li, Findings 2022)
ACL