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Register a new userImplicit Phenomena in Short-answer Scoring Data
الظواهر الضمنية في بيانات تسجيل الإجابة قصيرة الإجابة
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تعد تسجيل الإجابة القصيرة مهمة تقييم صحة نص قصير معين كاستجابة للسؤال الذي يمكن أن يأتي من مجموعة متنوعة من السيناريوهات التعليمية.كما هو المحتوى الوحيد، وليس النموذج، أمر مهم، يجب ألا يهم الصياغة الدقيقة بما في ذلك صريح الإجابة.ومع ذلك، فإن العديد من نماذج التسجيل الحديثة تعتمد بشدة على المعلومات المعجمية، سواء كانت تضمين كلمة في شبكة عصبية أو غرام N في SVM.وبالتالي، فإن الصياغة الدقيقة للإجابة قد تحدث فرقا كبيرا.لذلك نحن نحدد إلى أي مدى تحدث ظاهرة اللغة الضمنية في مجموعات بيانات الإجابة القصيرة وفحص التأثير لديهم على أداء التسجيل التلقائي.نجد أن مستوى الضمنية يعتمد على السؤال الفردي، وأن بعض الظواهر متكررة للغاية.حل الصياغة الضمنية للتركيز الصريح تميل بالفعل إلى تحسين أداء التسجيل التلقائي.
Short-answer scoring is the task of assessing the correctness of a short text given as response to a question that can come from a variety of educational scenarios. As only content, not form, is important, the exact wording including the explicitness of an answer should not matter. However, many state-of-the-art scoring models heavily rely on lexical information, be it word embeddings in a neural network or n-grams in an SVM. Thus, the exact wording of an answer might very well make a difference. We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance. We find that the level of implicitness depends on the individual question, and that some phenomena are very frequent. Resolving implicit wording to explicit formulations indeed tends to improve automatic scoring performance.
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