We propose Machines Talking To Machines (M2M), a framework combining
automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents
for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with
just a task schema and an API client from the dialogue system developer, but it
is also customizable to cater to task-specific interactions. Compared to the
Wizard-of-Oz approach for data collection, M2M achieves greater diversity and
coverage of salient dialogue flows while maintaining the naturalness of
individual utterances. In the first phase, a simulated user bot and a
domain-agnostic system bot converse to exhaustively generate dialogue
"outlines", i.e. sequences of template utterances and their semantic parses. In
the second phase, crowd workers provide contextual rewrites of the dialogues to
make the utterances more natural while preserving their meaning. The entire
process can finish within a few hours. We propose a new corpus of 3,000
dialogues spanning 2 domains collected with M2M, and present comparisons with
popular dialogue datasets on the quality and diversity of the surface forms and
dialogue flows.

提出了基于自动化和众包的 Machines Talking To Machines (M2M) 框架，可用于创建面向目标对话的端到端对话代理，该框架可适应特定任务，并能够收集更多不同领域内的对话流，并维持对话时的自然度，同时，提供了一个新的对话语料库，包含 3000 次对话，涵盖了 2 个领域，与流行的对话数据集进行了比较。