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.

该研究提出了一种可端到端训练的神经目标导向对话系统方法，能通过智能地将对话转移给人工服务代理来处理新用户行为。该方法的三个目标是：最大化用户任务成功率，最小化对人工服务代理的负担，并通过人工代理的反馈进行在线学习，以进一步减轻其负担。实验结果表明所提出的方法能够有效实现这些目标。