  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 (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 directions for
integrating human feedback in the NLP development loop.
