Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction. - Wanli Li 002; Tieyun Qian (CoRR, 2020)

Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction. - Wanli Li 002; Tieyun Qian; Xu Chen; Kejian Tang; Shaohui Zhan; Tao Zhan (IJCNN, 2021)

From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction. - Wanli Li 002; Tieyun Qian (CoRR, 2021)

Graph-based Model Generation for Few-Shot Relation Extraction. - Wanli Li 002; Tieyun Qian (EMNLP, 2022)

Generative Meta-Learning for Zero-Shot Relation Triplet Extraction. - Wanli Li 002; Tieyun Qian (CoRR, 2023)

Interactive Lexical and Semantic Graphs for Semisupervised Relation Extraction. - Wanli Li 002; Tieyun Qian; Ming Zhong 002; Xu Chen (IEEE Trans. Neural Networks Learn. Syst., 2023)

DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks. - Zeyu Zhang 004; Lu Li; Shuyan Wan; Sijie Wang; Zhiyi Wang; Zhiyuan Lu; Dong Hao; Wanli Li 002 (NeurIPS, 2024)

Adversarial Multi-Teacher Distillation for Semi-Supervised Relation Extraction. - Wanli Li 002; Tieyun Qian; Xuhui Li 001; Lixin Zou (IEEE Trans. Neural Networks Learn. Syst., 2024)

META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models. - Weicheng Li; Lixin Zou; Min Tang; Qing Yu; Wanli Li 002; Chenliang Li (COLING, 2025)

Generative Meta-Learning for Zero-Shot Relation Triplet Extraction. - Wanli Li 002; Tieyun Qian; Yi Song; Zeyu Zhang 004; Jiawei Li 008; Zhuang Chen 002; Lixin Zou (SIGIR, 2025)