Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality. Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system's capabilities.

中文翻译：

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具有大规模神经语言模型的协作讲故事

讲故事在人类社交和娱乐中起着核心作用。但是，许多有关自动叙事生成的研究都假设故事将由代理生成，而无需任何人工干预。在本文中，我们介绍了协作讲故事的任务，其中人工智能代理和一个人通过轮流添加到故事中来协作创建一个独特的故事。我们提供了一个协作式讲故事系统，该系统可与人类讲故事者合作，通过根据迄今为止的故事产生新的话语来创建故事。我们通过在写作提示及其伴随的小说作品的数据集上调整公开可用的大规模语言模型来构建叙事系统。我们将产生足够的类人话语识别为重要的技术问题，并提出了一种抽样和等级排序的方法来提高话语质量。定量评估表明，我们的方法优于基线，并且我们对系统功能进行了定性评估。