Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks

Hao-Chuan Kao, Man-Chen Hung, Lung-Hao Lee, Yuen-Hsien Tseng


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.
Anthology ID:
2021.rocling-1.33
Volume:
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Month:
October
Year:
2021
Address:
Taoyuan, Taiwan
Editors:
Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
257–264
Language:
URL:
https://aclanthology.org/2021.rocling-1.33
DOI:
Bibkey:
Cite (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).
Cite (Informal):
Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks (Kao et al., ROCLING 2021)
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PDF:
https://aclanthology.org/2021.rocling-1.33.pdf