TY - GEN
T1 - Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks
AU - Kao, Hao Chuan
AU - Hung, Man Chen
AU - Lee, Lung Hao
AU - Tseng, Yuen Hsien
N1 - Funding Information:
This work was partially supported by the Ministry of Science and Technology, Taiwan under the grant MOST 108-2218-E-008-017-MY3 and MOST 109-2410-H-003-123-MY3.
Publisher Copyright:
© 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Humor recognition
KW - Hypergraph neural networks
KW - Multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=85127449413&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85127449413
T3 - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
SP - 257
EP - 264
BT - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
A2 - Lee, Lung-Hao
A2 - Chang, Chia-Hui
A2 - Chen, Kuan-Yu
PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
T2 - 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Y2 - 15 October 2021 through 16 October 2021
ER -