Dialogue Planning via Brownian Bridge Stochastic Process for
  Goal-directed Proactive Dialogue
Jian Wang Dongding Lin Wenjie Li
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.