The DOI matches its Crossref record, with a small difference in title or year; 150 of the 151 references it cites were found in a registry or an index.
Survey on reinforcement learning for language processing
Open accessAbstract
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 (NLP) 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 NLP, 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 NLP that might benefit from RL.
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“For the verification subtask, verification models perform relatively well if given correct evidence [137].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [137]
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Cited by
Sorted by citations
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
“The purpose of RL is for the agent to learn an optimal or nearly-optimal policy that maximizes the reward that accumulates from the environment [91, 178].”
BackgroundDwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 1,001
A review on reinforcement learning for contact-rich robotic manipulation tasks
Elguea-Aguinaco, Í., Serrano-Muñoz, A., Chrysostomou, D., Inziarte-Hidalgo, I., Bøgh, S., & Arana-Arexolaleiba, N. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102517 · Cited by 177
A Systematic Study on Reinforcement Learning Based Applications
Sivamayilvelan, K., Rajasekar, E., Aljafari, B., Nikolovski, S., Subramaniyaswamy, V., & Indragandhi, V. (2023) · Energies · doi:10.3390/en16031512 · Cited by 197
Metadata
- Authors
- Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., & Wermter, S.
- Affiliations
- Universität Hamburg (DE)
- German Research Centre for Artificial Intelligence (DE)
- Corresponding author
- Uc-Cetina, Víctor
- Venue
- Artificial Intelligence Review
- Published
- 2022-06-03
- Volume
- 56
- Issue
- 2
- Pages
- 1543-1575
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3156829097
- Cited by
- 177
- References
- 151
- Citations by year
- 2026: 39 · 2025: 41 · 2024: 49 · 2023: 34 · 2022: 11
- FWCI
- 11.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Artificial Intelligence Review (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (TRR-169)
- CONACYT
- Universität Hamburg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- reinforcement learning, natural language processing, dialogue systems, deep reinforcement learning, NLP tasks
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