Advancements in Natural Language Processing: Enhancing Machine Understanding of Human Language in Conversational AI Systems

Abstract

This paper is designed to evaluate new advancements in Natural Language Processing (NLP) for the improvement of machine understanding of human language in the development of conversational AI systems. Using four key algorithms, which are the Transformers, Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Bidirectional Encoder Representations from Transformers (BERT), we discuss the output considering coherent and contextually relevant responses. The experimental results also revealed that the Transformer model yielded response accuracy of 92%, whereas the BERT model managed to achieve a precision of 89% against the RNNs and LSTMs at 83% and 81%, respectively. Second, it was found out that the addition of user feedback significantly enhanced the overall system performance by about 15%. This study describes the requirement of trustable, context-aware conversational agents and invites the integration of much more diverse language inputs than those applied up to now if a big group of users is to be addressed. Future directions illustrating the results of this study should be pursued in developing AI systems toward the improvement of explainability and adaptability so that interactions with machines can become more intuitive.