Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations. The key to achieving this task lies in planning dialogue paths that smoothly and coherently direct conversations towards the target. However, this is a challenging and under-explored task. In this work, we propose a coherent dialogue planning approach that uses a stochastic process to model the temporal dynamics of dialogue paths. We define a latent space that captures the coherence of goal-directed behavior using a Brownian bridge process, which allows us to incorporate user feedback flexibly in dialogue planning. Based on the derived latent trajectories, we generate dialogue paths explicitly using pre-trained language models. We finally employ these paths as natural language prompts to guide dialogue generation. Our experiments show that our approach generates more coherent utterances and achieves the goal with a higher success rate.

翻译：目标导向对话系统旨在通过多轮对话主动达成预定目标。实现这一任务的关键在于规划能够平滑且连贯地引导对话朝目标发展的对话路径。然而，这是一项具有挑战性且尚未充分探索的任务。在本工作中，我们提出了一种连贯的对话规划方法，利用随机过程对对话路径的时间动态性进行建模。我们定义了一个潜在空间，通过布朗桥过程捕捉目标导向行为的一致性，从而在对话规划中灵活融入用户反馈。基于推导出的潜在轨迹，我们使用预训练语言模型显式生成对话路径，并最终将这些路径作为自然语言提示指导对话生成。实验表明，我们的方法能生成更连贯的语句，并以更高的成功率达成目标。