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

Janarthanan Rajendran, Jatin Ganhotra, Lazaros C. Polymenakos


Abstract
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. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems that handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user’s task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential, and 3) learn online from the human agent’s responses to reduce human agents’ load further. We evaluate our proposed method on a modified-bAbI dialog task, which simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.
Anthology ID:
Q19-1024
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
Year:
2019
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
375–386
Language:
URL:
https://aclanthology.org/Q19-1024
DOI:
10.1162/tacl_a_00274
Bibkey:
Cite (ACL):
Janarthanan Rajendran, Jatin Ganhotra, and Lazaros C. Polymenakos. 2019. Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use. Transactions of the Association for Computational Linguistics, 7:375–386.
Cite (Informal):
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use (Rajendran et al., TACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/Q19-1024.pdf
Video:
 https://vimeo.com/384015139
Code
 IBM/modified-bAbI-dialog-tasks