84 citations · 129 across the 19 of their papers we have counts for
23 papers
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation
Xueliang Zhao, Yuxuan Wang, Chongyang Tao +2
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) th…
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Xueliang Zhao, Tingchen Fu, Chongyang Tao +1
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…
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…
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Tingchen Fu, Xueliang Zhao, Chongyang Tao +2
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous…
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
Yufei Wang, Can Xu, Qingfeng Sun +4
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains…
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao +5
Generating natural and informative texts has been a long-standing problem in NLP. Much effort has been dedicated into incorporating pre-trained language models (PLMs) with various…