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Register a new userPutting Humans in the Natural Language Processing Loop: A Survey
وضع البشر في حلقة معالجة اللغة الطبيعية: مسح
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كيف يمكننا تصميم أنظمة معالجة اللغة الطبيعية (NLP) التي تتعلم من ردود الفعل البشرية؟هناك هيئة بحثية متزايدة من أطر NLP البشرية (HITL) التي تدمج بشكل مستمر ردود الفعل الإنسانية لتحسين النموذج نفسه.Hitl NLP Research NLP NATCENT ولكن MultiriSious - حل مشاكل NLP المختلفة، وجمع تعليقات متنوعة من أشخاص مختلفين، وتطبيق أساليب مختلفة للتعلم من ردود الفعل الإنسانية.نقدم دراسة استقصا لمجتمعات Hitl NLP من كل من مجتمعات التعلم الآلي (ML) وتفاديا الإنسان (HCI) التي تسلط الضوء على تاريخها القصير الذي يلهم، ويلخص تماما الأطر الأخيرة التي تركز على مهامها وأهدافها والتفاعلات البشرية وتعلم ردود الفعلطرق.أخيرا، نناقش الدراسات المستقبلية لإدماج ردود فعل إنسانية في حلقة تطوير NLP.
How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks that continuously integrate human feedback to improve the model itself. HITL NLP research is nascent but multifarious---solving various NLP problems, collecting diverse feedback from different people, and applying different methods to learn from human feedback. We present a survey of HITL NLP work from both Machine Learning (ML) and Human-computer Interaction (HCI) communities that highlights its short yet inspiring history, and thoroughly summarize recent frameworks focusing on their tasks, goals, human interactions, and feedback learning methods. Finally, we discuss future studies for integrating human feedback in the NLP development loop.
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