Target-oriented proactive dialogue systems aim at leading 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.

面向目标的主动对话系统旨在将对话从对话上下文引导向预定目标，例如对指定项目提出建议或引入新的特定主题。为此，此类对话系统必须规划合理的行动以主动推动对话，同时规划适当的主题以将对话顺利推进到目标主题。在这项工作中，我们主要关注针对目标的对话生成的有效对话规划。受认知科学决策理论的启发，我们提出了一种新颖的目标约束双向规划（TRIP）方法，该方法通过向前看和向后看来规划适当的对话路径。通过将规划制定为生成任务，我们的 TRIP 使用两个 Transformer 解码器双向生成由一系列 <action, topic> 对组成的对话路径。他们应该相互监督，并通过最小化决策差距和目标的对比生成来集中一致的行动和主题。此外，我们提出了一种具有双向协议的目标约束解码算法，以更好地控制规划过程。随后，我们采用规划的对话路径以管道方式指导对话生成，其中我们探索了两种变体：基于提示的生成和计划控制的生成。在两个具有挑战性的对话数据集上进行了广泛的实验，这些数据集被重新用于探索面向目标的对话。我们的自动和人工评估表明，所提出的方法显着优于各种基线模型。