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.

通过收集多用户多 WOZ 数据集，该研究提出了多用户背景下的查询重写任务，旨在将两个用户之间的对话重写成只包含任务相关信息、能够被对话系统直接处理的简洁任务查询，实验证明通过预测的重写方式，显著提高了对话状态跟踪的性能，并且可以适用于未知领域。