Survey on reinforcement learning for language processing
By Víctor Uc Cetina, Nicolás Navarro-Guerrero, Anabel Martín-González, Cornelius Weber, Stefan Wermter. 2021
148 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pascale Fung
- Andrea Madotto
- Wenliang Dai
- Shunyu Yao
- Nayeon Lee
- Yan Xu
- Karthik Narasimhan
- Etsuko Ishii
In Natural Language Processing
- Pascale Fung
- Nayeon Lee
- Andrea Madotto
- Shunyu Yao
- Etsuko Ishii
- Yejin Bang
- Karthik Narasimhan
- John Yang
In Reinforcement Learning
- Shunyu Yao
- John Yang
- Karthik Narasimhan
- Howard Chen
- Andrea Madotto
- Pascale Fung
- Matthew J. Hausknecht
- Prithviraj Ammanabrolu
Citations in the corpus
- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022, background): “Recent advances in natural language processing (NLP) and reinforcement learning (RL) have brought about several exciting developments in agents that can perform sequential decision making while making use of linguistic context [30, 50, 58].”
- Survey of Hallucination in Natural Language Generation (2022, background): “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].”