Papers by Chaoqun Cui
Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model (2025.coling-main)
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| Challenge: | Pretrained Language Models excel in various Natural Language Processing tasks, but performance on social media applications like rumor detection remains suboptimal. |
| Approach: | They propose a pretraining strategy to infuse information from propagation structures into pretrained language models to capture interactions of stance and sentiment crucial for rumor detection. |
| Outcome: | The proposed model outperforms existing methods on social media applications and significantly improves rumor detection performance. |
Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods for rumor detection are limited in labeled data, but social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. |
| Approach: | They propose a framework for rumor detection with Graph Supervised Contrastive Learning that heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumors detection. |
| Outcome: | The proposed framework heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection. |
Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization (2025.acl-long)
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| Challenge: | Video dubbing systems use neural machine translation and text-to-speech technologies to translate original speech into visual media programs. |
| Approach: | They propose a preference optimization method to optimize video dubbing duration alignment . they propose combining segment-wise sampling and fine-grained loss to mitigate duration mismatches . |
| Outcome: | The proposed method achieves superior performance in duration alignment tasks. |