4 citations · 7 across the 5 of their papers we have counts for
5 papers
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
Quan Li, Tianxiang Zhao, Lingwei Chen +2
Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classificatio…
A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce
Jinhan Liu, Qiyu Chen, Junjie Xu +3
Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and…
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Zhanghao Hu, Yijun Yang, Junjie Xu +2
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa. This work challenges the existing question-answer encoding convention and explores…
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Anran Wu, Luwei Xiao, Xingjiao Wu +6
Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich…
Self-Explainable Graph Neural Networks for Link Prediction
Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critic…