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：本文研究了在低资源学习场景下针对虚构故事声明的验证问题。为此，我们生成了两个合成数据集，并开发了一个端到端流水线及其对应模型，在两个基准测试上进行了评估。为验证流水线有效性及基准测试难度，我们将模型结果与人工标注及随机分配结果进行了对比。代码已开源在 https://github.com/Derposoft/plot_hole_detection。