In this paper, we study the problem of claim verification in the context of claims about fictional stories in a low-shot learning setting. To this end, we generate two synthetic datasets and then develop an end-to-end pipeline and model that is tested on both benchmarks. To test the efficacy of our pipeline and the difficulty of benchmarks, we compare our models' results against human and random assignment results. Our code is available at https://github.com/Derposoft/plot_hole_detection.

翻译：在本文中，我们研究了在低资源学习环境下关于虚构故事的断言验证问题。为此，我们生成了两个合成数据集，并开发了一个端到端的流程和模型，对这两个基准进行了测试。为了测试我们的流程的有效性和分数的复杂性，我们将模型结果与人类和随机分配结果进行了比较。我们的代码可在 https://github.com/Derposoft/plot_hole_detection 进行查看。