DiactTOD：学习可泛化的潜在对话行为以用于可控任务型对话系统
DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems
AI总结：
本文提出端到端潜在对话行为模型DiactTOD，通过在潜在空间表示对话行为，在大型语料库预训练后实现零样本预测与控制回复生成，并在MultiWOZ数据集的多种设置下达到最先进性能。
摘要：
对话行为标注对于提升任务型对话系统的回复生成质量很重要。然而，由于不同数据集和任务可能存在不兼容的标注，以可泛化的方式使用对话行为控制回复生成具有挑战性。利用潜在动作空间或强化学习的替代方法虽不需要显式标注，但可能缺乏可解释性，或在定义任务特定奖励时面临困难。在本研究中，我们提出了一种新颖的端到端潜在对话行为模型（DiactTOD），在潜在空间中表示对话行为。DiactTOD 在大型语料库上预训练后，能够以零样本方式预测和控制对话行为，利用这些潜在表示生成可控回复。我们的方法在 MultiWOZ 数据集上的多种实验设置下均展现出最先进的性能，包括端到端和策略优化配置下的零样本、少样本和全数据微调。
英文摘要：
Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems. However, it can be challenging to use dialogue acts to control response generation in a generalizable way because different datasets and tasks may have incompatible annotations. While alternative methods that utilize latent action spaces or reinforcement learning do not require explicit annotations, they may lack interpretability or face difficulties defining task-specific rewards. In this work, we present a novel end-to-end latent dialogue act model (DiactTOD) that represents dialogue acts in a latent space. DiactTOD, when pre-trained on a large corpus, is able to predict and control dialogue acts to generate controllable responses using these latent representations in a zero-shot fashion. Our approach demonstrates state-of-the-art performance across a wide range of experimental settings on the MultiWOZ dataset, including zero-shot, few-shot, and full data fine-tuning with both end-to-end and policy optimization configurations.
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。
