20 citations · 52 across the 9 of their papers we have counts for
9 papers
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation
Siqi Bao, Huang He, Jun Xu +7
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy. To this end, we introdu…
Link the World: Improving Open-domain Conversation with Dynamic Spatiotemporal-aware Knowledge
Han Zhou, Xinchao Xu, Wenquan Wu +5
Making chatbots world aware in a conversation like a human is a crucial challenge, where the world may contain dynamic knowledge and spatiotemporal state. Several recent advances h…
Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation
Shihang Wang, Xinchao Xu, Wenquan Wu +3
Emotional support is a crucial skill for many real-world scenarios, including caring for the elderly, mental health support, and customer service chats. This paper presents a novel…
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
Zeming Liu, Jun Xu, Zeyang Lei +3
Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, an…
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Xinchao Xu, Zhibin Gou, Wenquan Wu +4
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations. The possible reason is that they lack the capability of understa…
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +2
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and…
Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Jun Xu, Zeyang Lei, Haifeng Wang +4
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dial…
Towards Conversational Recommendation over Multi-Type Dialogs
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +3
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e…
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Zhibin Liu, Zheng-Yu Niu, Hua Wu +1
Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can…