Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic} automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way. Our method can rapidly expand a small set of dialogue data with minimum or zero \textit{human involvement} and \textit{parameter update} and is thus much more cost-efficient and time-saving than crowdsourcing. Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed. When enough data is available, our method can still serve as an effective data augmentation method. Human evaluation results also show that our simulated dialogues have near-human fluency and annotation accuracy. The code and data are available at \textbf{\url{https://github.com/Leezekun/dialogic}}.

翻译：建立对话框系统需要大量附加说明的对话框。 这些数据集通常是通过众包创建的, 费用昂贵且耗时。 在本文中, 我们提议了\ textsc{ Dialogic}, 这是基于大型语言模型内文体学习的新式对话模拟方法, 以自动创建数据集。 种子用一些附加说明的对话框,\ textsc{ Dialogic} 自动选择演示的文本示例, 并促使 GPT-3 以可控制的方式生成新的对话框和说明。 我们的方法可以快速扩展小套对话数据, 其最小或零 \ textit{ 人类参与} 和\ textit{ parameter 更新}, 并且因此比众包更具有成本效益和节省时间。 MultiWOZ 数据集的实验结果显示, 在具有挑战性的低资源环境下, 使用同样数量的人类生成的对话, 且只有85个对话作为种子。 当数据可用时, 我们的方法仍然可以作为有效的数据增强方法, 接近数据增强 数据增强方法 。 人类评估的结果也是模拟的 。