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Survey on reinforcement learning for language processing
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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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