Abstract

Commonsense reasoning has emerged as a challenging problem in Artificial Intelligence (AI). However, one area of commonsense reasoning that has not received nearly as much attention in the AI research community is plausibility assessment, which focuses on determining the likelihood of commonsense statements. Human-annotated benchmarks are essential for advancing research in this nascent area, as they enable researchers to develop and evaluate AI models effectively. Because plausibility is a subjective concept, it is important to obtain nuanced annotations, rather than a binary label of ‘plausible’ or ‘implausible’. Furthermore, it is also important to obtain multiple human annotations for a given statement, to ensure validity of the labels.In this data article, we describe the process of re-annotating an existing commonsense plausibility dataset (SemEval-2020 Task 4) using large-scale crowdsourcing on the …

Metadata

publication

Data in Brief 57, 110869, 2024

year

2024

publication date

2024/12/1

authors

Navapat Nananukul, Ke Shen, Mayank Kejriwal

link

https://www.sciencedirect.com/science/article/pii/S2352340924008333

resource_link

https://www.sciencedirect.com/science/article/pii/S2352340924008333

journal

Data in Brief

volume

57

pages

110869

publisher

Elsevier