mlsanityData to 7 Oct 2026

Survey on reinforcement learning for language processing

arXiv 2104.05565 ↗First posted 12 Apr 2021cs.CL cs.AI cs.LGcited by 31

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Abstract (arXiv)

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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 76 of its 165 references that are in the map. First posted on arXiv 12 Apr 2021.

Builds on

76 of 165 references.

References · 76

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Citing papers per year: 2021: 1; 2022: 6; 2023: 8; 2024: 8; 2025: 3; 2026: 5.

31 papers.

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