text: 324,206,1385077064995270656,844013017830379522,Nayeon Lee,🚨Happy to share 2 papers accepted in #NAACL2021 to tackle misinformation 🔍👩‍✈️❌✅  We propose an UnifiedM2 model that unifies multiple domains of misinfo to learn a richer representation that aids effective few-shot learning of unseen misinfo tasks.1/n <LINK> <LINK> <LINK> Many thanks to all the co-authors 😇 @belindazli @sinongwang @pascalefung @gabema @scottyih @MadianKhabsa,https://arxiv.org/abs/2104.05243,"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, UnifiedM2learns 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 model's generalizability to unseen events. ",On Unifying Misinformation Detection,2,"['🚨Happy to share 2 papers accepted in #NAACL2021 to tackle misinformation 🔍👩\u200d✈️❌✅ \n\nWe propose an UnifiedM2 model that unifies multiple domains of misinfo to learn a richer representation that aids effective few-shot learning of unseen misinfo tasks.1/n\n\n<LINK> <LINK> <LINK>', 'Many thanks to all the co-authors 😇 @belindazli @sinongwang @pascalefung @gabema @scottyih @MadianKhabsa']",21,04,377