Survey on reinforcement learning for language processing

TL;DR

This survey reviews recent reinforcement learning methods applied to natural language processing, especially conversational systems, discussing their advantages, limitations, and future research directions.

Contribution

It provides a comprehensive overview of RL techniques in NLP, highlighting their applications, benefits, and challenges in conversational systems.

Findings

RL methods are increasingly used in conversational AI.

Advantages include improved adaptability and learning efficiency.

Limitations involve data requirements and model complexity.

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.