Target-constrained Bidirectional Planning for Generation of
  Target-oriented Proactive Dialogue
Jian Wang Dongding Lin Wenjie Li
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.