Deep Attention Diffusion Graph Neural Networks for Text Classification

Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, Xiaoyue Feng


Abstract
Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.
Anthology ID:
2021.emnlp-main.642
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8142–8152
Language:
URL:
https://aclanthology.org/2021.emnlp-main.642
DOI:
10.18653/v1/2021.emnlp-main.642
Bibkey:
Cite (ACL):
Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, and Xiaoyue Feng. 2021. Deep Attention Diffusion Graph Neural Networks for Text Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8142–8152, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Deep Attention Diffusion Graph Neural Networks for Text Classification (Liu et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.642.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.642.mp4
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