84 citations · 122 across the 11 of their papers we have counts for
7 papers · 1 filter
Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?
Yufan Zhao, Wei Wu, Can Xu
We study knowledge-grounded dialogue generation with pre-trained language models. Instead of pursuing new state-of-the-art on benchmarks, we try to understand if the knowledge stor…
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
Xueliang Zhao, Wei Wu, Can Xu +3
We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping respo…
StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Ze Yang, Wei Wu, Can Xu +5
Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for trainin…
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
Yufan Zhao, Can Xu, Wei Wu +1
We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved r…
Towards information-rich, logical text generation with knowledge-enhanced neural models
Hao Wang, Bin Guo, Wei Wu +1
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life. However, existing end-to-end neural mode…
Low-Resource Knowledge-Grounded Dialogue Generation
Xueliang Zhao, Wei Wu, Chongyang Tao +3
Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning su…