1 code implementation • 16 Oct 2022 • Jianing Wang, Wenkang Huang, Qiuhui Shi, Hongbin Wang, Minghui Qiu, Xiang Li, Ming Gao
In this paper, to address these problems, we introduce a seminal knowledge prompting paradigm and further propose a knowledge-prompting-based PLM framework KP-PLM.
1 code implementation • 11 May 2022 • Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts.
1 code implementation • 6 May 2022 • Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs).
no code implementations • 28 Mar 2014 • Duyu Tang, Bing Qin, Ting Liu, Qiuhui Shi
In order to analyze the emotional changes in accordance with time and space, this paper presents an Emotion Analysis Platform (EAP), which explores the emotional distribution of each province, so that can monitor the global pulse of each province in China.