Target-oriented proactive dialogue systems aim to lead conversations from a
dialogue context toward a pre-determined target, such as making recommendations
on designated items or introducing new specific topics. To this end, it is
critical for such dialogue systems to plan reasonable actions to drive the
conversation proactively, and meanwhile, to plan appropriate topics to move the
conversation forward to the target topic smoothly. In this work, we mainly
focus on effective dialogue planning for target-oriented dialogue generation.
Inspired by decision-making theories in cognitive science, we propose a novel
target-constrained bidirectional planning (TRIP) approach, which plans an
appropriate dialogue path by looking ahead and looking back. By formulating the
planning as a generation task, our TRIP bidirectionally generates a dialogue
path consisting of a sequence of <action, topic> pairs using two Transformer
decoders. They are expected to supervise each other and converge on consistent
actions and topics by minimizing the decision gap and contrastive generation of
targets. Moreover, we propose a target-constrained decoding algorithm with a
bidirectional agreement to better control the planning process. Subsequently,
we adopt the planned dialogue paths to guide dialogue generation in a pipeline
manner, where we explore two variants: prompt-based generation and
plan-controlled generation. Extensive experiments are conducted on two
challenging dialogue datasets, which are re-purposed for exploring
target-oriented dialogue. Our automatic and human evaluations demonstrate that
the proposed methods significantly outperform various baseline models.

以决策理论为灵感，提出了一种新颖的目标约束双向规划方法 (TRIP)，通过生成任务规划一个适当的对话路径，然后采用该路径指导对话生成的过程，实验证明该方法显著优于各种基线模型。