Papers by Chaokun Wang
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search (2022.emnlp-industry)
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| Challenge: | Existing approaches to e-commerce relevance matching ignore bipartite graphs in logs . experimental results show that proposed method improves human relevance judgment . |
| Approach: | They propose an efficient knowledge distillation framework for e-commerce relevance matching to exploit the advantages of Transformer-style and classical relevance matching models. |
| Outcome: | The proposed method significantly improves human relevance judgment on large-scale real-world data. |
Adaptive Text2GQL: Integrating Structural Twig Linking and Evolutionary In-Context Learning (2026.acl-long)
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| Challenge: | Existing approaches struggle with structural hallucinations and lack adaptability in cold-start scenarios. |
| Approach: | They propose a unified, training-free framework for translating natural language into Graph Query Languages. |
| Outcome: | The proposed framework improves accuracy and executability over baselines in Graph2GQLs. |
TeRDy: Temporal Relation Dynamics through Frequency Decomposition for Temporal Knowledge Graph Completion (2025.acl-long)
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| Challenge: | Existing methods for temporal knowledge graph completion struggle to capture long-term changes and short-term variability of relations. |
| Approach: | They propose a method that captures temporal relational dynamics by time-invariant embeddings and time-outvariant time-variant embeddedding. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets. |
LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)
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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li
| Challenge: | Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training. |
| Approach: | They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks. |
Adaptive and Robust Translation from Natural Language to Multi-model Query Languages (2025.acl-long)
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| Challenge: | Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically. |
| Approach: | They propose an adaptive Text-to-MMQL framework that includes a schema embedding module and an MMQl representation strategy to generate concise intermediate query formats with error correction in generated queries. |
| Outcome: | The proposed framework achieves over 9% accuracy improvement over baseline methods. |