1 code implementation • COLING 2022 • Chenxu Yang, Zheng Lin, Jiangnan Li, Fandong Meng, Weiping Wang, Lanrui Wang, Jie zhou
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
no code implementations • 7 Jun 2024 • Jiangnan Li, Zheng Lin, Lanrui Wang, Qingyi Si, Yanan Cao, Mo Yu, Peng Fu, Weiping Wang, Jie zhou
Besides, EDEN can help LLMs achieve better recognition of emotions and causes, which explores a new research direction of explainable emotion understanding in dialogues.
no code implementations • 26 Nov 2023 • Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Xiaolei Huang, Yanan Cao, Jingang Wang, Weiping Wang
Recently, there has been a heightened interest in building chatbots based on Large Language Models (LLMs) to emulate human-like qualities in dialogues, including expressing empathy and offering emotional support.
no code implementations • 13 Oct 2023 • Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context.
1 code implementation • 21 Oct 2022 • Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng, Chenxu Yang, Weiping Wang, Jie zhou
We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.