End-to-End Personalized Humorous Response Generation in Untrimmed Multi-Role Dialogue System

End-to-End Personalized Humorous Response Generation in Untrimmed Multi-Role Dialogue System

Multi-role dialogue is challenging in natural language processing (NLP), which needs not only to understand sentences but also to simulate interaction among roles. However, the existing methods assume that only two speakers are present in a conversation. In real life, this assumption is not always v...

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Bibliographic Details
Main Authors: Qichuan Yang, Zhiqiang He, Zhiqiang Zhan, Rang Li, Yanwei Lee, Yang Zhang, Changjian Hu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Dialogue generation
multi-role conversation
neural network
reinforcement learning
speaker prediction
Online Access:https://ieeexplore.ieee.org/document/8755858/

Internet

https://ieeexplore.ieee.org/document/8755858/

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