How to build and use dialogue data efficiently, and how to deploy models in
different domains at scale can be two critical issues in building a
task-oriented dialogue system. In this paper, we propose a novel manual-guided
dialogue scheme to alleviate these problems, where the agent learns the tasks
from both dialogue and manuals. The manual is an unstructured textual document
that guides the agent in interacting with users and the database during the
conversation. Our proposed scheme reduces the dependence of dialogue models on
fine-grained domain ontology, and makes them more flexible to adapt to various
domains. We then contribute a fully-annotated multi-domain dataset MagDial to
support our scheme. It introduces three dialogue modeling subtasks: instruction
matching, argument filling, and response generation. Modeling these subtasks is
consistent with the human agent's behavior patterns. Experiments demonstrate
that the manual-guided dialogue scheme improves data efficiency and domain
scalability in building dialogue systems. The dataset and benchmark will be
publicly available for promoting future research.

本文提出了一种新的手动引导对话方案，其中代理从对话和手册学习任务，从而降低了对领域本体的依赖，使其更灵活地适应不同的领域，相关实验表明该方案提高了对话系统的数据效率和领域可扩展性