7 papers
Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesis
Zheng Dou, Zhao Zhang, Hao Geng +2
Retrieval-augmented scientific synthesis aims to answer complex research questions by integrating information from multiple papers into comprehensive and well-grounded responses. S…
Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
Wei Chen, Xingyu Guo, Shuang Li +4
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs but is challenged by complex, multi-faceted distributional shifts. Existing…
Learning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation
Wei Chen, Xingyu Guo, Shuang Li +7
Graph Domain Adaptation (GDA) aims to bridge distribution shifts between domains by transferring knowledge from well-labeled source graphs to given unlabeled target graphs. One pro…
UniFAR: A Unified Facet-Aware Retrieval Framework for Scientific Documents
Zheng Dou, Zhao Zhang, Deqing Wang +2
Scientific document retrieval (SDR) plays a critical role in modern scientific research, supporting knowledge discovery and evidence-based reasoning. It has evolved along two parad…
FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis
Wei Chen, Zhao Zhang, Meng Yuan +2
In this paper, we address the task of targeted sentiment analysis (TSA), which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresp…
Bridging Social Psychology and LLM Reasoning: Conflict-Aware Meta-Review Generation via Cognitive Alignment
Wei Chen, Han Ding, Meng Yuan +3
The rapid growth of scholarly submissions has overwhelmed traditional peer review systems, driving the need for intelligent automation to preserve scientific rigor. While large lan…
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
Wei Chen, Guo Ye, Yakun Wang +5
Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graph…