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Register a new userLearning to Decompose and Organize Complex Tasks
تعلم تحلل وتنظيم المهام المعقدة
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يعتمد الأشخاص على أدوات إدارة المهام الرقمية، مثل البريد الإلكتروني أو التطبيقات المهمة، لإدارة مهامهم. بعض هذه المهام كبيرة ومعقدة، مما يؤدي إلى حل الشلل والمشاعر التي تغلبت على جزء المستخدم. أظهر أدب الأدبيات الدقيقة أن هذه المهام قد تستفيد من التحلل والتنظيم، من أجل تقليل الحمل المعرفي المستخدم. وبالتالي، في هذه الورقة، نقترح خط أنابيب نهاية إلى نهاية جديدة يستهلك مهمة معقدة وتحفيز رسم بياني للاعتماد من نص غير منظم لتمثيل المهام الفرعية وعلاقاتها. يجد حلنا أولا عقد في المهام الفرعية من العديد من المقالات حول الويب عن طريق حقن مولد نص عصبي مع ثلاثة Key Desiderata - الصلة، التجريد، والتوافق الآراء. ثم نقوم بحل الحواف والنتائج بين هذه العقد الفرعية من خلال تعلم العلاقات التبعية المهمة. نقوم بجمع مجموعة بيانات جديدة من المهام المعقدة مع الرسم البياني المهمة الفرعية لتطوير وتقييم حلولنا. يتم تقييم كل من مكونين حل الحث الرسمي الخاص بنا في التجارب، مما يدل على أن نماذجنا تتفوق على مولد نصي نصي على درجة كبيرة. يحتوي حلنا التعميم والقابل للتطوير على آثار مهمة على تعزيز إنتاجية المستخدم والمساعدة في إدارة المهام الرقمية.
People rely on digital task management tools, such as email or to-do apps, to manage their tasks. Some of these tasks are large and complex, leading to action paralysis and feelings of being overwhelmed on the part of the user. The micro-productivity literature has shown that such tasks could benefit from being decomposed and organized, in order to reduce user cognitive load. Thus in this paper, we propose a novel end-to-end pipeline that consumes a complex task and induces a dependency graph from unstructured text to represent sub-tasks and their relationships. Our solution first finds nodes for sub-tasks from multiple how-to' articles on the web by injecting a neural text generator with three key desiderata -- relevance, abstraction, and consensus. Then we resolve and infer edges between these subtask nodes by learning task dependency relations. We collect a new dataset of complex tasks with their sub-task graph to develop and evaluate our solutions. Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly. Our generalizable and scalable end-to-end solution has important implications for boosting user productivity and assisting with digital task management.
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