Proactive dialogue system is able to lead the conversation to a goal topic
and has advantaged potential in bargain, persuasion and negotiation. Current
corpus-based learning manner limits its practical application in real-world
scenarios. To this end, we contribute to advance the study of the proactive
dialogue policy to a more natural and challenging setting, i.e., interacting
dynamically with users. Further, we call attention to the non-cooperative user
behavior -- the user talks about off-path topics when he/she is not satisfied
with the previous topics introduced by the agent. We argue that the targets of
reaching the goal topic quickly and maintaining a high user satisfaction are
not always converge, because the topics close to the goal and the topics user
preferred may not be the same. Towards this issue, we propose a new solution
named I-Pro that can learn Proactive policy in the Interactive setting.
Specifically, we learn the trade-off via a learned goal weight, which consists
of four factors (dialogue turn, goal completion difficulty, user satisfaction
estimation, and cooperative degree). The experimental results demonstrate I-Pro
significantly outperforms baselines in terms of effectiveness and
interpretability.

本研究旨在提高主动对话策略在动态用户交互中的自然性和实用性，特别关注非合作用户行为，提出一种名为 I-Pro 的新解决方案，在学习目标权重方面有所创新，并在实验中取得了显著的效果和可解释性。

与非合作用户的交互：积极对话策略的新范式

Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy

A proactive dialogue system has the ability to proactively lead the
conversation. Different from the general chatbots which only react to the user,
proactive dialogue systems can be used to achieve some goals, e.g., to
recommend some items to the user. Background knowledge is essential to enable
smooth and natural transitions in dialogue. In this paper, we propose a new
multi-task learning framework for retrieval-based knowledge-grounded proactive
dialogue. To determine the relevant knowledge to be used, we frame knowledge
prediction as a complementary task and use explicit signals to supervise its
learning. The final response is selected according to the predicted knowledge,
the goal to achieve, and the context. Experimental results show that explicit
modeling of knowledge prediction and goal selection can greatly improve the
final response selection. Our code is available at
this https URL

本文提出了一种新的多任务学习框架，以预测相关知识并通过显式信号来监督其学习，实现知识驱动的主动式对话，从而提高最终响应选择的准确性。