Reinforcement Learning on Social Chabot to Improve the Semantic Variation

Abstract

Artificial intelligence (AI) in the fields of conversation and language has experienced immense growth as a result of the large quantities of text corpora available for training models. This paper discusses conversational AI dialogue systems concerning the components of natural language processing (NLP) and reinforcement learning that function together to produce a human-like response. The types of conversational AI, namely, task-oriented systems, question-answering agents, and social chatbots, are also expanded on to provide a systematic review. Reinforcement learning has a crucial role in resolving errors in existing conversational AI models and allows for a factor of exploration in the art of communication. The trial-and-error nature of reinforcement learning agents ensures that irrelevant patterns in a text corpus are not memorized. The implementation of reinforcement learning and NLP tools to create social chatbots with emotional intelligence brings researchers one step closer to mimicking human conversation.