Low-Shot Learning for Fictional Claim Verification
This work addresses claim verification for fictional narratives, which is a domain-specific and incremental contribution to low-shot learning.
The paper tackled the problem of verifying claims about fictional stories in a low-shot learning setting by generating two synthetic datasets and developing an end-to-end pipeline and model, which was tested against human and random baselines to assess efficacy and benchmark difficulty.
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.