NAACL 2021long27 citations
On Unifying Misinformation Detection
Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, Madian Khabsa
Abstract
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2’s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model’s generalizability to unseen events.
BibTeX
@inproceedings{lee-etal-2021-unifying,
title = "On Unifying Misinformation Detection",
author = "Lee, Nayeon and
Li, Belinda Z. and
Wang, Sinong and
Fung, Pascale and
Ma, Hao and
Yih, Wen-tau and
Khabsa, Madian",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.432/",
doi = "10.18653/v1/2021.naacl-main.432",
pages = "5479--5485"
}