Survey on reinforcement learning for language processing

In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. Th...

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Bibliographic Details
Published inarXiv.org
Main Authors Uc-Cetina, Victor, Navarro-Guerrero, Nicolas, Martin-Gonzalez, Anabel, Weber, Cornelius, Wermter, Stefan
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.03.2022
Subjects
Algorithms
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
Computer Science - Learning
Machine learning
Natural language processing
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Summary:In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning.
ISSN:2331-8422
DOI:10.48550/arxiv.2104.05565