1 code implementation • 29 Aug 2014 • Zongcheng Ji, Zhengdong Lu, Hang Li
Human computer conversation is regarded as one of the most difficult problems in artificial intelligence.
no code implementations • 9 Aug 2019 • Zongcheng Ji, Qiang Wei, Hua Xu
Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community.
no code implementations • CVPR 2021 • Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
no code implementations • ACL 2021 • Zongcheng Ji, Tian Xia, Mei Han, Jing Xiao
Disease is one of the fundamental entities in biomedical research.
no code implementations • Findings (EMNLP) 2021 • Yanmeng Wang, Jun Bai, Ye Wang, Jianfei Zhang, Wenge Rong, Zongcheng Ji, Shaojun Wang, Jing Xiao
To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage.
no code implementations • 23 Aug 2023 • Hengyuan Zhang, Peng Chang, Zongcheng Ji
This research marks the first application of large language models to table-based question answering tasks, enhancing the model's comprehension of both table structures and content.
no code implementations • NAACL (SMM4H) 2021 • Zongcheng Ji, Tian Xia, Mei Han
This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.