Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

TACL 2019 Janarthanan RajendranJatin GanhotraLazaros Polymenakos

Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.