3 citations · 7 across the 7 of their papers we have counts for
8 papers
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
Wangjie Jiang, Zhihao Ye, Zijing Ou +7
Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensi…
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Jiayuan Ding, Tong Xiang, Zijing Ou +5
Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean pa…
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
Zijing Ou, Qinliang Su, Jianxing Yu +3
Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely mo…
Prototypical Graph Contrastive Learning
Shuai Lin, Pan Zhou, Zi-Yuan Hu +6
Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generall…
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
Zijing Ou, Qinliang Su, Jianxing Yu +5
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality ha…
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
Yi Cheng, Siyao Li, Bang Liu +4
This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this ta…
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
Zhengxu Hou, Bang Liu, Ruihui Zhao +4
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the spa…
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses
Yi Liu, Xingliang Yuan, Ruihui Zhao +3
Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. In practice, these S…