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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Putting Humans in the Natural Language Processing Loop: A Survey
%A Wang, Zijie J.
%A Choi, Dongjin
%A Xu, Shenyu
%A Yang, Diyi
%Y Blodgett, Su Lin
%Y Madaio, Michael
%Y O’Connor, Brendan
%Y Wallach, Hanna
%Y Yang, Qian
%S Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-putting
%X 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.
%U https://aclanthology.org/2021.hcinlp-1.8
%P 47-52

Markdown (Informal)

[Putting Humans in the Natural Language Processing Loop: A Survey](https://aclanthology.org/2021.hcinlp-1.8) (Wang et al., HCINLP 2021)

• Putting Humans in the Natural Language Processing Loop: A Survey (Wang et al., HCINLP 2021)

ACL

• Zijie J. Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021. Putting Humans in the Natural Language Processing Loop: A Survey. In Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing, pages 47–52, Online. Association for Computational Linguistics.