与非合作用户互动：主动对话策略的新范式
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
摘要：
主动对话系统能够将对话引导到目标话题上，在讨价还价、说服和谈判中具有潜在的优势。现有的基于语料库的学习方式限制了其在现实世界中的实际应用。为此，我们致力于将积极对话政策的研究推进到一个更自然和更具挑战性的环境，即，与用户动态交互。此外，我们提请注意非合作用户行为-用户谈论的路径外的话题时，他/她不满意的代理介绍了以前的主题。我们认为，快速达到目标主题和保持高用户满意度的目标并不总是收敛的，因为接近目标的主题和用户偏好的主题可能不一样。针对这个问题，我们提出了一个新的解决方案命名为I-Pro，可以学习主动策略的交互式设置。具体来说，我们通过学习目标权重，其中包括四个因素（对话轮，目标完成难度，用户满意度估计，合作程度）的权衡。实验结果表明，I-Pro在有效性和可解释性方面明显优于基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
