NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
Abstract
Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation and disinformation in the news. However, most existing work has focused on claim sentence analysis while overlooking additional crucial attributes (e.g., the claimer and the main object associated with the claim). In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain. We extend the claim detection problem to include extraction of additional attributes related to each claim and release 889 claims annotated over 143 news articles. NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. To this end, we see that zero-shot and prompt-based baselines show promising performance on this benchmark, while still considerably behind human performance.
Cite
@article{arxiv.2112.08544,
title = {NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge},
author = {Revanth Gangi Reddy and Sai Chetan and Zhenhailong Wang and Yi R. Fung and Kathryn Conger and Ahmed Elsayed and Martha Palmer and Preslav Nakov and Eduard Hovy and Kevin Small and Heng Ji},
journal= {arXiv preprint arXiv:2112.08544},
year = {2022}
}
Comments
Accepted at EMNLP 2022