313 citations · 624 across the 26 of their papers we have counts for
26 papers
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank
Jiduan Liu, Jiahao Liu, Qifan Wang +6
Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning…
Learning to Express in Knowledge-Grounded Conversation
Xueliang Zhao, Tingchen Fu, Chongyang Tao +3
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studie…
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…