面向目标导向主动式对话生成的目标约束双向规划
Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue
AI总结：
针对目标导向主动对话的规划难题，提出带双向一致性的目标约束双向规划TRIP方法，用两个Transformer解码器双向生成动作-话题对路径，引导对话生成，在两个数据集上显著优于基线模型。
摘要：
目标导向的主动式对话系统旨在引导对话从当前语境朝着预设目标推进，例如推荐指定商品或引入特定新话题。为此，这类对话系统的核心在于规划合理的行动以主动驱动对话，同时规划恰当的话题以推动对话平稳过渡至目标话题。本研究重点聚焦于目标导向对话生成中的高效对话规划问题。受认知科学中的决策理论启发，我们提出了一种全新的目标约束双向规划（TRIP）方法，该方法通过前瞻与回溯双向推演来规划合理的对话路径。通过将规划任务形式化为生成任务，我们的TRIP方法利用两个Transformer解码器，双向生成由一系列<动作，话题>对组成的对话路径。两个解码器通过最小化决策差异与目标对比生成的方式实现相互监督，最终在一致的动作与话题上达成收敛。此外，我们提出了一种带双向一致性约束的目标约束解码算法，以更好地管控规划过程。随后，我们采用规划得到的对话路径以流水线方式引导对话生成，并探索了两种实现变体：基于提示的生成与规划控制的生成。我们在两个经过改造、用于探索目标导向对话的高难度对话数据集上开展了大量实验。自动评估与人工评估的结果均表明，所提出的方法显著优于各类基线模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
