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Register a new userSemantic Categorization of Social Knowledge for Commonsense Question Answering
تصنيف الدلالي للمعرفة الاجتماعية للحصول على سؤال الرد على الرد
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أدت طرازات اللغة الكبيرة المدربة مسبقا (PLMS) إلى نجاح كبير في مهام الإجابة على الأسئلة المختلفة (QA) في أزياء نهاية إلى نهاية.ومع ذلك، تم إيلاء القليل من الاهتمام وفقا لمعرفة المعرفة المنطقية لتمييز مهام ضمان الجودة هذه.في هذا العمل، اقترحنا تصنيف الدلالات اللازمة لهذه المهام باستخدام SocialIQA كمثال.بناء على فئات المعرفة الاجتماعية الخاصة بنا المسمى DataSet على رأس SocialiQa، نربط نماذج QA العصبية لدمج فئات المعرفة الاجتماعية هذه ومعلومات العلاقة بين قاعدة المعرفة.على عكس العمل السابق، نلاحظ نماذجنا مع تصنيفات دلالية للمعرفة الاجتماعية يمكن أن تحقق أداء مماثل مع نموذج بسيط نسبيا وحجم أصغر مقارنة بالمناهج المعقدة الأخرى.
Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion. However, little attention has been paid to what commonsense knowledge is needed to deeply characterize these QA tasks. In this work, we proposed to categorize the semantics needed for these tasks using the SocialIQA as an example. Building upon our labeled social knowledge categories dataset on top of SocialIQA, we further train neural QA models to incorporate such social knowledge categories and relation information from a knowledge base. Unlike previous work, we observe our models with semantic categorizations of social knowledge can achieve comparable performance with a relatively simple model and smaller size compared to other complex approaches.
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