Papers by Shuigeng Zhou
Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)
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| Challenge: | Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them . |
| Approach: | They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them . |
| Outcome: | The proposed method outperforms existing methods on long- and short-text datasets. |
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for data augmentation do not fully exploit the potential of DA in NLP. |
| Approach: | They propose an easy and plug-in framework for data augmentation to support effective text classification. |
| Outcome: | The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks. |
VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)
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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou
| Challenge: | Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy. |
| Approach: | They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems. |
| Outcome: | The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better. |
Multi-matrix Factorization Attention (2025.findings-acl)
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| Challenge: | Existing variants for Multi-Head Attention (MHA) fail to maintain strong performance under stringent Key-Value cache (KV cache) constraints. |
| Approach: | They propose to use multi-matrix factorization attention and MFA-Key-reuse attention architectures to increase model capacity under tight KV cache constraints. |
| Outcome: | The proposed architecture outperforms existing methods while reducing KV cache usage by 56% and 93.7% in large-scale experiments. |