Many real-world open-domain conversation applications have specific goals to
achieve during open-ended chats, such as recommendation, psychotherapy,
education, etc. We study the problem of imposing conversational goals on
open-domain chat agents. In particular, we want a conversational system to chat
naturally with human and proactively guide the conversation to a designated
target subject. The problem is challenging as no public data is available for
learning such a target-guided strategy. We propose a structured approach that
introduces coarse-grained keywords to control the intended content of system
responses. We then attain smooth conversation transition through turn-level
supervised learning, and drive the conversation towards the target with
discourse-level constraints. We further derive a keyword-augmented conversation
dataset for the study. Quantitative and human evaluations show our system can
produce meaningful and effective conversations, significantly improving over
other approaches.

本研究提出了一种基于关键词控制的方法，通过监督学习和话语级别的限制引导自然对话流向指定目标，我们通过创建增强的关键词对话数据集进行定量和人类评估，证明了我们的系统相对于其他方法可以产生有意义且有效的对话。