Papers by Xiaoyue Feng
Deep Attention Diffusion Graph Neural Networks for Text Classification (2021.emnlp-main)
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| Challenge: | Existing methods for text classification based on graph neural networks (GNNs) consider only one-hop neighborhoods and low-frequency information within texts, which suffer from over-smoothing issues if many graph layers are stacked. |
| Approach: | They propose a deep attention diffusion Graph Neural Network model to learn text representations by bridging the chasm of interaction difficulties between a word and its distant neighbors. |
| Outcome: | The proposed model outperforms existing methods on standard benchmark datasets on a set of textual features. |