@inproceedings{d8fbd1613eb4413ba29fb6ffdefaf07f,
title = "GENERATING PERSONA-AWARE EMPATHETIC RESPONSES WITH RETRIEVAL-AUGMENTED PROMPT LEARNING",
abstract = "Empathetic response generation requires perceiving and understanding the user's emotion to deliver suitable responses. However, existing models generally lack an ability to respond in a persona-specific way, which has been shown to play a vital role in expressing appropriate empathy. To address this problem, we propose a novel Transformer-based architecture that incorporates retrieval-augmented prompt learning to generate persona-aware empathetic responses. Since personalized emotional resonance is subtle and uncontrollable, we employ dense passage retrieval to retrieve exemplary responses that reflect specific persona and context characteristics to cue the generative model on signaling empathy. Extensive experiments confirm the effectiveness of our model for persona-aware empathetic response generation.",
keywords = "Dialogue Generation, Empathetic, Exemplar Prompting, Natural Language Processing, Personalized",
author = "Zhengjie Huang and Pingsheng Liu and \{de Melo\}, Gerard and Liang He and Linlin Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10447417",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "12441--12445",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
address = "美国",
}