A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling
A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling
复制标题
用于独立于领域、可参数化讲故事的叙事句子规划器和结构器
作者：
S. Lukin;M. Walker
讲故事是日常生活中不可或缺的一部分，也是我们分享信息和与他人联系的关键部分。使用自然语言生成 (NLG) 生成适合个人读者的故事的能力可能会对许多不同的应用产生巨大影响。然而，迄今为止这尚未成为现实的原因之一是 NLG 故事差距，即故事生成引擎生成的计划型表示与 NLG 引擎所需的语言表示之间的脱节。在这里，我们描述 Fabula Tales，一个支持故事生成和 NLG 的讲故事系统。通过使用直观的用户界面对现有故事中的文本进行手动注释，Fabula Tales 会自动提取底层故事表示及其附带的基于句法的表示。叙述学和句子规划参数应用于这些结构以生成故事的不同版本。我们展示了我们的讲故事系统如何在句子级别以及话语级别改变故事。我们还通过在《伊索寓言》和社交媒体上发布的第一人称博客上测试我们的方法，表明我们的方法可以应用于不同类型的故事。此类故事的内容和类型差异很大，这支持了我们的主张，即我们的方法是通用的且独立于领域的。然后，我们进行了多项用户研究来评估生成的故事变体，并表明 Fabula Tales 自动生成的变体被认为更直接、有趣和正确，并且优于不使用叙事参数的基线生成系统。
Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesop’s Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Tales’ automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters.