Work

Low-Shot Learning for Fictional Claim Verification

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2304.02769

Authors: Chadalapaka, Viswanath, Nguyen, Derek, Choi, JoonWon, Joshi, Shaunak, Rostami, Mohammad

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4362705359

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