من بين المهام التي تحفزها انتشار المعلومات الخاطئة، فإن اكتشاف الدعاية تحديا بشكل خاص بسبب عجز التعليقات التوضيحية الدقيقة الدقيقة اللازمة لتدريب نماذج التعلم الآلي.هنا نظهر كيف يمكن الاستفادة من البيانات من المهام الأخرى ذات الصلة، بما في ذلك تقييم المصداقية، في إطار التعلم متعدد المهام (MTL) لتسريع عملية التدريب.وتحقيقا لهذه الغاية، نقوم بتصميم نموذج يستند إلى بيرت مع طبقات إخراج متعددة، وتدريبه في العديد من سيناريوهات MTL وأداء التقييم ضد معيار الذهب السائم.
Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.
References used
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