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Register a new userEvaluating Deception Detection Model Robustness To Linguistic Variation
تقييم نموذج الكشف عن الخداع متواضع على التباين اللغوي
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مع الاستخدام المتزايد لأحكام الخوارزميات المدفوعة بالجهاز، من الأهمية بمكان تطوير النماذج القوية في المدخلات المتطورة أو التلاعب بها.نقترح تحليلا واسع النطاق من المتانة النموذجي ضد التباين اللغوي في تحديد الكشف الأخبار الخادع، وهي مهمة مهمة في سياق المعلومات الخاطئة المنتشرة عبر الإنترنت.نحن نفكر في مهام التنبؤ ومقارنة ثلاثة من المدينات الحديثة لتسليط الضوء على الاتجاهات المتسقة في الأداء النموذجي، وتظليل الثقة العالي، والإخفاقات عالية التأثير.من خلال قياس فعالية استراتيجيات الدفاع المشددي وتقييم الحساسية النموذجية للهجمات الخصومة باستخدام نص غير مضطرب للشخصية، نجد أن الطابع أو النماذج المختلطة هي الدفاعات الأكثر فعالية وأن تكتيكات الهجوم القائم على الاضطرابات الأكثر نجاحا.
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensive analysis of model robustness against linguistic variation in the setting of deceptive news detection, an important task in the context of misinformation spread online. We consider two prediction tasks and compare three state-of-the-art embeddings to highlight consistent trends in model performance, high confidence misclassifications, and high impact failures. By measuring the effectiveness of adversarial defense strategies and evaluating model susceptibility to adversarial attacks using character- and word-perturbed text, we find that character or mixed ensemble models are the most effective defenses and that character perturbation-based attack tactics are more successful.
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