Papers by Guanghui Ma
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Open-Topic False Information Detection on Social Networks with Contrastive Adversarial Learning (2022.emnlp-main)
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| Challenge: | Existing models for false information detection on social networks are too harsh for actual social networks that contain both seen and unseen topics simultaneously. |
| Approach: | They propose an open-topic scenario that assumes that all test data topics are seen or unseen by the model, but which is too harsh for actual social networks that contain both seen and unseened topics simultaneously. |
| Outcome: | The proposed model improves on two benchmark datasets and a variety of graph neural networks on two social networks and shows that it is more accurate than existing models. |