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Register a new userNews Headline Grouping as a Challenging NLU Task
عنوان الأخبار تجميع مهمة NLU صعبة
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لقد شهد التقدم الأخير في فهم اللغة الطبيعية (NLU) أحدث النماذج تفوق الأداء البشري على العديد من المهام القياسية. قادت هذه النتائج المثيرة للإعجاب المجتمع إلى التقاطية بشأن قيود البيانات، وتكرر التحديات الأكثر دقة. في هذه الورقة، نقدم مهمة تجميع العنوان الرئيسي (HLG) ومجموعة بيانات مقابلة (HLGD) تتكون من 20،056 أزواج من عناوين الأخبار، والتي تم تسمية كل منها بحكم ثنائي فيما إذا كان الزوج ينتمي إلى نفس المجموعة. على HLGD، يحقق المعلقون البشري الأداء العالي حوالي 0.9 F-1، في حين تصل نماذج المحولات الحالية من المحولات الحالية إلى 0.75 F-1، وفتح المسار لمزيد من التحسينات. نقترحنا كذلك نموذج مبادلة عناوين رئيسية رواية غير مدهشة لمهمة تجميع العنوان الرئيسي الذي يحقق في غضون 3 F-1 من أفضل النموذج الإشرافي. أخيرا، نقوم بتحليل نماذج عالية الأداء مع اختبارات الاتساق، وتجد أن النماذج ليست متسقة في تنبؤاتها، وكشف عن حدود النمذجة من الهندسة الحالية.
Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks. These impressive results have led the community to introspect on dataset limitations, and iterate on more nuanced challenges. In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group. On HLGD, human annotators achieve high performance of around 0.9 F-1, while current state-of-the art Transformer models only reach 0.75 F-1, opening the path for further improvements. We further propose a novel unsupervised Headline Generator Swap model for the task of HeadLine Grouping that achieves within 3 F-1 of the best supervised model. Finally, we analyze high-performing models with consistency tests, and find that models are not consistent in their predictions, revealing modeling limits of current architectures.
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