A Review of Reinforcement Learning for Natural Language Processing, and
Applications in Healthcare
- URL: http://arxiv.org/abs/2310.18354v1
- Date: Mon, 23 Oct 2023 20:26:15 GMT
- Title: A Review of Reinforcement Learning for Natural Language Processing, and
Applications in Healthcare
- Authors: Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou,
Fang Wang, Rama Hoetzlein, Rui Zhang
- Abstract summary: Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems.
This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare.
We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction.
- Score: 17.38031305999901
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reinforcement learning (RL) has emerged as a powerful approach for tackling
complex medical decision-making problems such as treatment planning,
personalized medicine, and optimizing the scheduling of surgeries and
appointments. It has gained significant attention in the field of Natural
Language Processing (NLP) due to its ability to learn optimal strategies for
tasks such as dialogue systems, machine translation, and question-answering.
This paper presents a review of the RL techniques in NLP, highlighting key
advancements, challenges, and applications in healthcare. The review begins by
visualizing a roadmap of machine learning and its applications in healthcare.
And then it explores the integration of RL with NLP tasks. We examined dialogue
systems where RL enables the learning of conversational strategies, RL-based
machine translation models, question-answering systems, text summarization, and
information extraction. Additionally, ethical considerations and biases in
RL-NLP systems are addressed.
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