论文标题
与大规模神经语言模型的合作讲故事
Collaborative Storytelling with Large-scale Neural Language Models
论文作者
论文摘要
讲故事在人类社交和娱乐中起着核心作用。但是，关于自动讲故事的大部分研究都假定故事将由代理商而没有任何人类互动。在本文中，我们介绍了合作讲故事的任务，在该任务中，人工智能代理商和一个人合作通过轮流加入它来创建一个独特的故事。我们提出了一个合作的讲故事系统，该系统与人类讲故事的人合作，通过迄今为止基于故事的新话语来创建故事。我们通过在写作提示的数据集及其随附的虚构作品的数据集上调整公开可用的大规模语言模型来构建讲故事系统。我们确定产生足够类似人类的话语是一个重要的技术问题，并提出了一种样本和秩的方法来提高话语质量。定量评估表明，我们的方法的表现优于基准，我们对系统能力进行了定性评估。
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.
