论文标题
在叙事一代中连贯和一致地使用实体
Towards Coherent and Consistent Use of Entities in Narrative Generation
论文作者
论文摘要
大型的预训练的语言模型（LMS）在产生长长的文本方面表现出了令人印象深刻的能力。但是，关于它们保持实体连贯性和一致性的能力几乎没有分析。在这项工作中，我们专注于叙事发电的最终任务，并系统地分析了生成故事的远程实体连贯性和一致性。首先，我们提出了一组自动指标，以根据实体使用来衡量模型性能。鉴于这些指标，我们量化了当前LMS的局限性。接下来，我们通过使用辅助实体相关的损失来指导读取并将其写入内存，以端到端的方式提出以动态实体内存的增强预训练的LM。我们证明，动态实体记忆会根据自动和人类的判断提高实体连贯性，并帮助保存与实体相关的信息，尤其是在有限上下文窗口的设置中。最后，我们还验证了我们的自动指标与人类评分相关，并可以很好地表明产生的故事的质量。
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maintain entity coherence and consistency. In this work, we focus on the end task of narrative generation and systematically analyse the long-range entity coherence and consistency in generated stories. First, we propose a set of automatic metrics for measuring model performance in terms of entity usage. Given these metrics, we quantify the limitations of current LMs. Next, we propose augmenting a pre-trained LM with a dynamic entity memory in an end-to-end manner by using an auxiliary entity-related loss for guiding the reads and writes to the memory. We demonstrate that the dynamic entity memory increases entity coherence according to both automatic and human judgment and helps preserving entity-related information especially in settings with a limited context window. Finally, we also validate that our automatic metrics are correlated with human ratings and serve as a good indicator of the quality of generated stories.
