4 citations · 6 across the 5 of their papers we have counts for
5 papers
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Yonghao Liu, Mengyu Li, Fausto Giunchiglia +4
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph model…
A Simple Graph Contrastive Learning Framework for Short Text Classification
Yonghao Liu, Fausto Giunchiglia, Lan Huang +3
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined…
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Yonghao Liu, Mengyu Li, Wei Pang +4
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical sce…
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Yonghao Liu, Fausto Giunchiglia, Ximing Li +3
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among rese…
Layers of technology in pluriversal design. Decolonising language technology with the LiveLanguage initiative
Gertraud Koch, Gábor Bella, Paula Helm +1
Language technology has the potential to facilitate intercultural communication through meaningful translations. However, the current state of language technology is deeply entangl…