1 code implementation • 30 May 2024 • Kuo Liao, Shuang Li, Meng Zhao, Liqun Liu, Mengge Xue, Zhenyu Hu, Honglin Han, Chengguo Yin
To address this limitation, we propose a novel Reinforcement Learning framework enhanced with Label-sensitive Reward (RLLR) to amplify the performance of LLMs in NLU tasks.
no code implementations • 21 Jul 2021 • Zhongyang Li, Xiao Ding, Kuo Liao, Bing Qin, Ting Liu
Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.
no code implementations • SEMEVAL 2020 • Xiao Ding, Dingkui Hao, Yuewei Zhang, Kuo Liao, Zhongyang Li, Bing Qin, Ting Liu
In this task, we dedicate to detecting causation, especially counterfactuals from texts.
1 code implementation • IJCNLP 2019 • Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan
Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.
no code implementations • 18 Jul 2019 • Xiao Ding, Zhongyang Li, Ting Liu, Kuo Liao
The evolution and development of events have their own basic principles, which make events happen sequentially.