NEWSCLAIMS: A New Benchmark for Claim Detection from News with Attribute Knowledge
Document Type
Conference Proceeding
Publication Title
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
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.
First Page
6002
Last Page
6018
Publication Date
1-1-2022
Recommended Citation
R. Reddy et al., "NEWSCLAIMS: A New Benchmark for Claim Detection from News with Attribute Knowledge," Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, pp. 6002 - 6018, Jan 2022.