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Putting Humans in the Natural Language Processing Loop: A Survey
[article]
2021
arXiv
pre-print
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 collected feedback. We present a survey of HITL NLP work from both Machine Learning
arXiv:2103.04044v1
fatcat:bnwj25lwofcwrnjtvlta64niq4