no code implementations • COLING 2022 • BoWen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, Liwen Jing
SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL).
no code implementations • 1 Sep 2024 • Fuqiang Niu, Zebang Cheng, Xianghua Fu, Xiaojiang Peng, Genan Dai, Yin Chen, Hu Huang, BoWen Zhang
To address this, we introduce a new multimodal multi-turn conversational stance detection dataset (called MmMtCSD).
no code implementations • 2 Jul 2024 • BoWen Zhang, Zhichao Huang, Genan Dai, Guangning Xu, Xiaomao Fan, Hu Huang
\method{} comprises several key modules, including the core subgraph knowledge submodule, graph domain adaptation module, and few-shot learning module for downstream tasks.
no code implementations • 1 May 2024 • Sicheng Zhao, Hui Chen, Hu Huang, Pengfei Xu, Guiguang Ding
Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains.
no code implementations • 26 Dec 2023 • BoWen Zhang, Daijun Ding, Liwen Jing, Hu Huang
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets.
no code implementations • 6 Apr 2023 • BoWen Zhang, Xianghua Fu, Daijun Ding, Hu Huang, Yangyang Li, Liwen Jing
Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media.
no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.