5 citations · 7 across the 14 of their papers we have counts for
16 papers
Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
Zhijun Zhang, Qianlong Wang, Keyang Ding +5
Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synthetic data generation, producin…
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Hu Huang, Genan Dai, Fuqiang Niu +3
Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We…
A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection
Genan Dai, Zini Chen, Yi Yang +1
Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task, from zero-shot prompting t…
ToPT: Task-Oriented Prompt Tuning for Urban Region Representation Learning
Zitao Guo, Changyang Jiang, Tianhong Zhao +3
Learning effective region embeddings from heterogeneous urban data underpins key urban computing tasks (e.g., crime prediction, resource allocation). However, prevailing two-stage…
Rethinking Fusion: Disentangled Learning of Shared and Modality-Specific Information for Stance Detection
Zhiyu Xie, Fuqiang Niu, Genan Dai +4
Multi-modal stance detection (MSD) aims to determine an author's stance toward a given target using both textual and visual content. While recent methods leverage multi-modal fusio…
Geometry-Aware Spiking Graph Neural Network
Bowen Zhang, Genan Dai, Hu Huang +1
Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through…
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuqiang Niu, Zini Chen, Zhiyu Xie +3
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduc…
MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection
Fuqiang Niu, Genan Dai, Yisha Lu +4
In the realm of contemporary social media, automatic stance detection is pivotal for opinion mining, as it synthesizes and examines user perspectives on contentious topics to uncov…
Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
Bowen Zhang, Jun Ma, Fuqiang Niu +3
Zero-shot stance detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with…
C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset
Fuqiang Niu, Yi Yang, Xianghua Fu +2
Stance detection has become an essential tool for analyzing public discussions on social media. Current methods face significant challenges, particularly in Chinese language proces…
A Survey of Stance Detection on Social Media: New Directions and Perspectives
Bowen Zhang, Genan Dai, Fuqiang Niu +5
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these…
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
Fuqiang Niu, Zebang Cheng, Xianghua Fu +5
Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse mu…
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
Yongcheng Li, Lingcong Cai, Ying Lu +8
Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variabili…
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Yongcheng Li, Lingcong Cai, Ying Lu +7
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a nove…
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning
Bowen Zhang, Zhichao Huang, Genan Dai +3
Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as s…
A Logically Consistent Chain-of-Thought Approach for Stance Detection
Bowen Zhang, Daijun Ding, Liwen Jing +1
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets. Incorporating background knowledge to enhance transferability between seen and unseen targets consti…