163 citations · 753 across the 42 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…
Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li, Can Xu, Wei Wu +3
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often r…
Open Domain Dialogue Generation with Latent Images
Ze Yang, Wei Wu, Huang Hu +3
We consider grounding open domain dialogues with images. Existing work assumes that both an image and a textual context are available, but image-grounded dialogues by nature are mo…
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…