9 citations · 14 across the 4 of their papers we have counts for
4 papers
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang, Lu Li, Shuyan Wan +5
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign predicti…
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
Wanli Li, Tieyun Qian, Yi Song +4
Zero-shot Relation Triplet Extraction (ZeroRTE) aims to extract relation triplets from texts containing unseen relation types. This capability benefits various downstream informati…
From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction
Wanli Li, Tieyun Qian
Lack of labeled data is a main obstacle in relation extraction. Semi-supervised relation extraction (SSRE) has been proven to be a promising way for this problem through annotating…
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
Wanli Li, Tieyun Qian
Manual annotation of the labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have arous…