Training Millions of Personalized Dialogue Agents

Pierre-Emmanuel Mazaré
Pierre-Emmanuel Mazaré
Martin Raison
Martin Raison

EMNLP, pp. 2775-2779, 2018.

Cited by: 56|Bibtex|Views124
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. ...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments