While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat between two users that is semantically and pragmatically consistent with the original user utterance, thus resulting in the same dialogue state and system response. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios, e.g., social chatter and deliberation. Supported by this data, we propose the novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query that retains only task-relevant information and that is directly consumable by the dialogue system. We demonstrate that in multi-user dialogues, using predicted rewrites substantially improves dialogue state tracking without modifying existing dialogue systems that are trained for single-user dialogues. Further, this method surpasses training a medium-sized model directly on multi-user dialogues and generalizes to unseen domains.

翻译：尽管大多数任务型对话假设系统每次只与单一用户交互，但对话系统日益需要与多个同时进行协作决策的用户通信。为促进此类系统的开发，我们发布多用户MultiWOZ数据集：包含两名用户与一个系统之间的任务型对话。该数据集通过将MultiWOZ 2.2中每条用户话语替换为两名用户间语义与语用层面均与原话语一致的小规模对话，从而保持相同的对话状态与系统响应。这些对话反映了任务场景中协作决策的有趣动态，例如社交闲聊与商讨。基于该数据，我们提出多用户上下文查询重写的全新任务：将两名用户间的任务导向对话重写为仅保留任务相关信息的简洁查询，使其可直接被对话系统消费。实验表明，在多用户对话系统中，使用预测的重写结果可在不修改现有面向单用户场景训练的对话系统情况下，显著提升对话状态追踪性能。此外，该方法优于直接使用多用户对话数据训练中等规模模型，并能泛化至未见领域。