Large corpora of task-based and open-domain conversational dialogues are hugely valuable in the field of data-driven dialogue systems. Crowdsourcing platforms, such as Amazon Mechanical Turk, have been an effective method for collecting such large amounts of data. However, difficulties arise when task-based dialogues require expert domain knowledge or rapid access to domain-relevant information, such as databases for tourism. This will become even more prevalent as dialogue systems become increasingly ambitious, expanding into tasks with high levels of complexity that require collaboration and forward planning, such as in our domain of emergency response. In this paper, we propose CRWIZ: a framework for collecting real-time Wizard of Oz dialogues through crowdsourcing for collaborative, complex tasks. This framework uses semi-guided dialogue to avoid interactions that breach procedures and processes only known to experts, while enabling the capture of a wide variety of interactions. The framework is available at https://github.com/JChiyah/crwiz

中文翻译：

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CRWIZ：众包实时绿野仙踪对话的框架

大量基于任务和开放域的对话式对话在数据驱动的对话系统领域非常有价值。众包平台，例如 Amazon Mechanical Turk，一直是收集如此大量数据的有效方法。然而，当基于任务的对话需要专家领域知识或快速访问领域相关信息（例如旅游数据库）时，就会出现困难。随着对话系统变得越来越雄心勃勃，扩展到需要协作和前瞻性规划的高度复杂的任务，例如在我们的应急响应领域，这将变得更加普遍。在本文中，我们提出了 CRWIZ：一个框架，用于通过众包收集实时的绿野仙踪对话，用于协作、复杂的任务。该框架使用半引导式对话来避免违反只有专家知道的程序和流程的交互，同时能够捕获各种交互。该框架可在 https://github.com/JChiyah/crwiz 获得