Search Results for author: Jinfeng Zhou

Found 10 papers, 6 papers with code

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation

1 code implementation18 Aug 2022 Jinfeng Zhou, Chujie Zheng, Bo wang, Zheng Zhang, Minlie Huang

Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy.

Dialogue Generation Empathetic Response Generation +1

Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach

1 code implementation16 Jul 2023 Jinfeng Zhou, Zhuang Chen, Bo wang, Minlie Huang

Experiments verify the superiority of Supporter in achieving positive emotion elicitation during responding while maintaining conversational goals including coherence.

CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs

no code implementations EMNLP 2021 Jinfeng Zhou, Bo wang, Ruifang He, Yuexian Hou

Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths.

Knowledge Graphs Recommendation Systems +1

TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph

no code implementations COLING 2022 Zhitong Yang, Bo wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target.

Response Generation

Aligning Recommendation and Conversation via Dual Imitation

no code implementations5 Nov 2022 Jinfeng Zhou, Bo wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations.

Recommendation Systems

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