Building a Conversational Agent Overnight with Dialogue Self-Play
Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür, Abhinav Rastogi, Ankur Bapna, Neha Nayak, Larry Heck Google AI Mountain View, CA, USA Correspondence to
Abstract
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.
中文速览
让对话系统快速适配新任务一直是个难题——既没有现成数据，靠人工"绿野仙踪"收集又慢又贵还容易漏掉关键场景。"机器对机器"框架（Machines Talking To Machines，M2M）把这件事拆成两步：先让一个模拟用户机器人和一个有限状态机系统机器人自动"对话自演"，穷举出各种对话流程的模板骨架（outline）；再让众包工人把这些骨架中的机械模板句改写成自然语言，同时保留原有的语义标注，从而省去昂贵的人工标注环节。整套流程只需要开发者提供任务的槽位schema和一个API客户端，几小时内就能产出带标注的对话数据集。作者用M2M收集了横跨两个领域共3000条对话，与MultiWOZ等主流数据集对比后发现，M2M在对话流程覆盖度和语言多样性上均有优势，为低成本、高质量地冷启动目标导向对话系统提供了一条切实可行的路径。
原文 arXiv:1801.04871;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1801.04871v1