Low-Shot Learning for Fictional Claim Verification
Artificial Intelligence
2023-04-07 v1
Abstract
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.
Cite
@article{arxiv.2304.02769,
title = {Low-Shot Learning for Fictional Claim Verification},
author = {Viswanath Chadalapaka and Derek Nguyen and JoonWon Choi and Shaunak Joshi and Mohammad Rostami},
journal= {arXiv preprint arXiv:2304.02769},
year = {2023}
}
Comments
6 pages