An affect-rich neural conversational model with biased attention and weighted cross-entropy loss

• Main Authors: Zhong, Peixiang, Wang, Di, Miao, Chunyan
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Computation and Language; Artificial Intelligence
• Online Access: https://arxiv.org/abs/1811.07078; https://hdl.handle.net/10356/139252
• Institution: Nanyang Technological University

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has be...