@inproceedings{843ec803d3974dedaae3c68e347e6a44,
title = "Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks",
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.",
keywords = "Humor recognition, Hypergraph neural networks, Multi-label classification",
author = "Kao, {Hao Chuan} and Hung, {Man Chen} and Lee, {Lung Hao} and Tseng, {Yuen Hsien}",
note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",
year = "2021",
language = "English",
series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "257--264",
editor = "Lung-Hao Lee and Chia-Hui Chang and Kuan-Yu Chen",
booktitle = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
}