70 citations · 103 across the 11 of their papers we have counts for
6 papers · 1 filter
InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Kunze Wang, Soyeon Caren Han, Josiah Poon
Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co…
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Kunze Wang, Soyeon Caren Han, Siqu Long +1
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks.…
Few-shot Forgery Detection via Guided Adversarial Interpolation
Haonan Qiu, Siyu Chen, Bei Gan +4
The increase in face manipulation models has led to a critical issue in society - the synthesis of realistic visual media. With the emergence of new forgery approaches at an unprec…
Understanding Graph Convolutional Networks for Text Classification
Soyeon Caren Han, Zihan Yuan, Kunze Wang +2
Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings.…
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
Yubo Ma, Zehao Wang, Yixin Cao +4
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there i…
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
Yuanhan Zhang, Qinghong Sun, Yichun Zhou +7
Large-scale datasets play a vital role in computer vision. But current datasets are annotated blindly without differentiation to samples, making the data collection inefficient and…