[2104.05565] Survey on reinforcement learning for language processing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2104.05565 (cs) [Submitted on 12 Apr 2021 ( v1 ), last revised 15 Mar 2022 (this version, v3)] Title: Survey on reinforcement learning for language processing Authors: Victor Uc-Cetina , Nicolas Navarro-Guerrero , Anabel Martin-Gonzalez , Cornelius Weber , Stefan Wermter View a PDF of the paper titled Survey on reinforcement learning for language processing, by Victor Uc-Cetina and 4 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2104.05565 [cs.CL] (or arXiv:2104.05565v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2104.05565 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Artificial Intelligence Review 2022 Related DOI : https://doi.org/10.1007/s10462-022-10205-5 Focus to learn more DOI(s) linking to related resources Submission history From: Victor Uc-Cetina [ view email ] [v1] Mon, 12 Apr 2021 15:33:11 UTC (5,992 KB) [v2] Mon, 14 Mar 2022 17:00:00 UTC (1,449 KB) [v3] Tue, 15 Mar 2022 21:02:38 UTC (1,447 KB) Full-text links: Access Paper: View a PDF of the paper titled Survey on reinforcement learning for language processing, by Victor Uc-Cetina and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2021-04 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Nicolás Navarro-Guerrero Cornelius Weber Stefan Wermter export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from