activity
20122022
most citedHow does Disagreement Help Generalization against Label Corruption?

154 citations · 479 across the 24 of their papers we have counts for

collaborators

57 papers

cs.CL20225 cited

Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs

Bowen Xing, Ivor W. Tsang

Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the d…

cs.CL20222 cited

Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

Bowen Xing, Ivor W. Tsang

Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions. However, they treat all labels and label embeddi…

cs.CL2022

Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning

Bowen Xing, Ivor W. Tsang

Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task. However, we discover that existing syntax-based models su…

cs.IR2022

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

Yan Zhang, Changyu Li, Ivor W. Tsang +5

Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve th…

cs.CL2022

DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition

Bowen Xing, Ivor W. Tsang

The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. I…

cs.LG2021

Edge but not Least: Cross-View Graph Pooling

Xiaowei Zhou, Jie Yin, Ivor W. Tsang

Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been develope…