@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T On Unifying Misinformation Detection
%A Lee, Nayeon
%A Li, Belinda Z.
%A Wang, Sinong
%A Fung, Pascale
%A Ma, Hao
%A Yih, Wen-tau
%A Khabsa, Madian
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F lee-etal-2021-unifying
%X 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.
%R 10.18653/v1/2021.naacl-main.432
%U https://aclanthology.org/2021.naacl-main.432
%U https://doi.org/10.18653/v1/2021.naacl-main.432
%P 5479-5485
Markdown (Informal)
[On Unifying Misinformation Detection](https://aclanthology.org/2021.naacl-main.432) (Lee et al., NAACL 2021)
ACL
- Nayeon Lee, Belinda Z. Li, Sinong Wang, Pascale Fung, Hao Ma, Wen-tau Yih, and Madian Khabsa. 2021. On Unifying Misinformation Detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5479–5485, Online. Association for Computational Linguistics.