Next-Generation NLP Techniques: Boosting Machine Understanding in Conversational AI Technologies

Abstract

AI has become a game changer in conversational AI technologies that has improved and advanced Natural Language Processing (NLP) in various domains. This research explores next-generation NLP techniques that boost machine comprehension in conversational AI, focusing on four key algorithms: Some are called Transformer, GPT, BERT and T5. The accuracy, computational requirements, and context awareness achieved while implementing these models on conversational data are discussed in this study. The analysis of data reveals that the model which has the highest accuracy is the BERT model with the accuracy of 92. 3% had shown improved in language comprehension and GPT had the highest score of 90 in response generation most of which were well put together. 5% coherence score. Transformer based models also showed better scalability and the data processing time reduced by 30% as compared to the earlier models. The paper also has a section where findings are compared with related work where there is an improvement of 15% in issues accuracy and issues computational efficiency by a 20%. These developments in the NLP techniques can be considered as breakthroughs in the conversational AI providing new possibilities in creation of smarter and more effective interfaces. There are obviously some shortcomings in ethical concerns and it is expected that future work will consequently improve these models for specific domains.