@inproceedings{jiang-etal-2021-multi,
title = "Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning",
author = "Jiang, Shuoran and
Chen, Qingcai and
Liu, Xin and
Hu, Baotian and
Zhang, Lisai",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.513",
doi = "10.18653/v1/2021.acl-long.513",
pages = "6563--6573",
abstract = "Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model.",
}
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%0 Conference Proceedings
%T Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning
%A Jiang, Shuoran
%A Chen, Qingcai
%A Liu, Xin
%A Hu, Baotian
%A Zhang, Lisai
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F jiang-etal-2021-multi
%X Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model.
%R 10.18653/v1/2021.acl-long.513
%U https://aclanthology.org/2021.acl-long.513
%U https://doi.org/10.18653/v1/2021.acl-long.513
%P 6563-6573
Markdown (Informal)
[Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning](https://aclanthology.org/2021.acl-long.513) (Jiang et al., ACL-IJCNLP 2021)
ACL