.png){kind=icon}
返回
Survey on reinforcement learning for language processing
.png){kind=icon}
OA
AI
DOI：10.1007/s10462-022-10205-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Reinforcement learning
Natural language processing
Conversational systems
Parsing
Translation
Text generation
期刊
IF:
13.9
论文数:
6.1K
被引数:
1.9W
机构
引用论文
.png){kind=icon}
Sodium channel kinetic changes that produce Brugada syndrome or progressive cardiac conduction system disease产生Brugada综合征或进行性心脏传导系统疾病的钠通道动力学变化
PREAI
分享收藏分享收藏分享收藏
查看更多内容