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

## TL;DR

This paper introduces an end-to-end trainable goal-oriented dialog system that intelligently transfers conversations to human agents to maximize task success and minimize human workload, adapting to new user behaviors during deployment.

## Contribution

It presents a novel method that learns online to transfer dialogs to human agents only when necessary, improving real-world applicability of neural dialog systems.

## Key findings

- Effective transfer to human agents at test time.
- Reduces human agent workload.
- Maintains high user task success.

## 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 which 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 that 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.