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.
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 • 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 • ACL 2021 • Zongcheng Ji, Tian Xia, Mei Han, Jing Xiao
Disease is one of the fundamental entities in biomedical research.
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 • 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.
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.