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Register a new userSemEval-2021 Task 12: Learning with Disagreements
Semeval-2021 المهمة 12: التعلم مع الخلافات
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الخلاف بين المبرمجين هو في كل مكان في جميع مجموعات البيانات المشروحة بأحكام بشرية في كل من معالجة اللغة الطبيعية ورؤية الكمبيوتر.ومع ذلك، تفترض معظم أساليب تعلم الآلات الأكثر إشرافا أن التفسير المفضل الوحيد موجود لكل عنصر، وهو في أحسن الأحوال مثالية.كان الهدف من مهمة Semeval-2021 المشتركة بشأن التعلم مع الخلافات (LE-WI-I-DI) هو توفير إطار اختبار موحد لأساليب التعلم من البيانات التي تحتوي على شروح متعددة وربما متناقضة تغطي مجموعات البيانات الأكثر شهرة التي تحتوي على معلومات حول الخلافاتتفسير اللغة وتصنيف الصور.في هذه الورقة وصفنا المهمة المشتركة ونتائجها.
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.
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