Reinforcement Learning in Natural Language Processing: A Survey

Reinforcement learning (RL) is a powerful technique for learning from data and feedback, but its effective application to natural language processing (NLP) tasks remains an open question. Consequently, this paper first introduces the general concepts of RL and the common approaches. Subsequently, we review the task construction settings and the application of RL for various NLP problems, such as machine translation, dialogue system, and text generation. Finally, we discuss some promising research directions and challenges of RL in NLP. We hope that our work can provide a comprehensive overview and inspire more research on this promising yet challenging topic.