Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.
翻译:以语言建模方式制作故事图案,试图从一种语言模型(LM)中取样来构建一个图案,以预测下一个字符、字词或句子来添加故事。LM技术缺乏接受用户指导以达到具体目标的能力,导致故事没有清晰的进展感和缺乏一致性。我们展示了一种奖赏分层技术,分析故事文集并产生中间奖项,这些奖项被反馈到预先培训的LM中,以引导模型走向一个特定目标。自动化评估显示,我们的技术可以创造出一个模型,生成故事文集,始终实现一个特定目标。人类研究显示,所产生的故事比基线地块生成技术更合理。