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Register a new userMulti-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event
التعلم متعدد المهام لتمكين تحديد الموقع تحديد الهوية في الساعات الأولى من حدث الأزمات
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يتطلب تدريب نموذج التعلم العميق القوي والموثوق كمية كبيرة من البيانات.في مجال الأزمات، يبني نماذج تعليمية عميقة لتحديد المعلومات القابلة للتنفيذ من التدفق الضخم للبيانات المنشورة من قبل شهود فعاليات الأزمات على وسائل التواصل الاجتماعي، في غضون فترة حرجة، هي مركزية لعمليات الاستجابة السريعة والإغاثة.ومع ذلك، فإن بناء مجموعة بيانات كبيرة ومصفحة لتدريب نماذج التعلم العميق ليست ممكنة دائما في وضع الأزمات.في هذه الورقة، نحقق في نهج تعليمي متعدد المهام لزيارة البيانات المشروحة المتاحة المتاحة لعدة مهام ذات صلة من مجال الأزمات لتحسين الأداء في مهمة رئيسية مع بيانات مخفية محدودة.على وجه التحديد، نركز على استخدام التعلم متعدد المهام لتحسين الأداء في مهمة تحديد الموقع في تغريدات الأزمات.
Training a robust and reliable deep learning model requires a large amount of data. In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations. However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation. In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data. Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.
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