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        <title>Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks</title>
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        <namePart type="given">Hao-Chuan</namePart>
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        <namePart type="given">Yuen-Hsien</namePart>
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            <namePart type="given">Kuan-Yu</namePart>
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    <abstract>We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally, we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.</abstract>
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%0 Conference Proceedings
%T Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks
%A Kao, Hao-Chuan
%A Hung, Man-Chen
%A Lee, Lung-Hao
%A Tseng, Yuen-Hsien
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F kao-etal-2021-multi
%X We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally, we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.
%U https://aclanthology.org/2021.rocling-1.33
%P 257-264

Markdown (Informal)

[Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks](https://aclanthology.org/2021.rocling-1.33) (Kao et al., ROCLING 2021)

• Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks (Kao et al., ROCLING 2021)

ACL

• Hao-Chuan Kao, Man-Chen Hung, Lung-Hao Lee, and Yuen-Hsien Tseng. 2021. Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 257–264, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).