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Register a new userTowards Realistic Single-Task Continuous Learning Research for NER
نحو دراسة التعلم المستمرة واقعية واقعية ل NER
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هناك اهتمام متزايد بالتعلم المستمر (CL)، حيث أصبحت خصوصية البيانات أولوية للتطبيقات الحقيقية لتعلم الآلة في العالم.وفي الوقت نفسه، لا يزال هناك نقص في معايير NLP الأكاديمية التي تنطبق على إعدادات CL واقعية، وهي تحدي كبير للنهوض بالمجال.في هذه الورقة، نناقش بعض خصائص البيانات غير الواقعية لمجموعات البيانات العامة، ودراسة تحديات التعلم المستمر واقعي واقعي وكذلك فعالية بروفات البيانات كوسيلة للتخفيف من خسارة الدقة.نحن نبني مجموعة بيانات CL NER من مجموعة بيانات موجودة متوفرة للجمهور وإصدارها جنبا إلى جنب مع الكود إلى مجتمع البحث.
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack of academic NLP benchmarks that are applicable for realistic CL settings, which is a major challenge for the advancement of the field. In this paper we discuss some of the unrealistic data characteristics of public datasets, study the challenges of realistic single-task continuous learning as well as the effectiveness of data rehearsal as a way to mitigate accuracy loss. We construct a CL NER dataset from an existing publicly available dataset and release it along with the code to the research community.
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