Papers by Renhong Cheng
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (D19-1)
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| Challenge: | Existing methods for aspect-specific sentiment classification are noisy and downgraded performance. |
| Approach: | They propose a constrained attention network to regularize attention for multi-aspect sentiment analysis by orthogonal regularization on multiple aspects and sparse regularization for each single aspect. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets and extends to multi-task settings. |
Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)
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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng
| Challenge: | Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries. |
| Approach: | They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. |
| Outcome: | The proposed framework outperforms strong baselines while being robust against various NOTA rates. |
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)
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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
| Challenge: | Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. |
| Approach: | They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs. |
| Outcome: | The proposed method significantly outperforms existing temporal knowledge graph embedding models. |
TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)
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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng
| Challenge: | Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users. |
| Approach: | They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification. |
| Outcome: | The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots. |
Density-Aware Prototypical Network for Few-Shot Relation Classification (2023.findings-emnlp)
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| Challenge: | Existing studies treat NOTA as an extra class and treat it the same as known relations. |
| Approach: | They propose a density-aware prototypical network to treat various instances distinctly . they separate known instances and isolate NOTA instances, respectively . their code will be made public after the paper is accepted . |
| Outcome: | The proposed method outperforms strong baselines with robustness towards different NOTA rates. |
Multi-Label Few-Shot Learning for Aspect Category Detection (2021.acl-long)
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| Challenge: | Existing few-shot learning methods focus on single-label predictions, which can not work well for ACD since a sentence may contain multiple aspect categories. |
| Approach: | They propose a few-shot learning method that uses the prototypical network to learn aspects from a set of aspects. |
| Outcome: | The proposed method significantly outperforms baseline methods on three datasets. |
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (D19-1)
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| Challenge: | Existing approaches to cross-domain sentiment classification focus on domain-invariant representations, but few focus on the domain-specific information. |
| Approach: | They propose to distill domain-invariant sentiment features with an orthogonal domain-dependent task . the orthogonalist task is built on the aspects varying widely in different domains . |
| Outcome: | The proposed method improves domain-invariant features and transfer performance on three public datasets. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |
ECoK: Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024.findings-acl)
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Zhunheng Wang, Xiaoyi Liu, Mengting Hu, Rui Ying, Ming Jiang, Jianfeng Wu, Yalan Xie, Hang Gao, Renhong Cheng
| Challenge: | Existing knowledge graphs focus on the representation and reasoning of general factual knowledge, while there are significant deficiencies in the understanding and reasoning for emotional knowledge. |
| Approach: | They propose a commonsense knowledge graph that can be used to represent emotional knowledge by combining theories from psychology, cognitive science, and linguistics. |
| Outcome: | The proposed model surpasses GPT-4-Turbo in the emotion-related tasks. |