  Named Entity Recognition and Intent Classification are among the most
important subfields of the field of Natural Language Processing. Recent
research has lead to the development of faster, more sophisticated and
efficient models to tackle the problems posed by those two tasks. In this work
we explore the effectiveness of two separate families of Deep Learning networks
for those tasks: Bidirectional Long Short-Term networks and Transformer-based
networks. The models were trained and tested on the ATIS benchmark dataset for
both English and Greek languages. The purpose of this paper is to present a
comparative study of the two groups of networks for both languages and showcase
the results of our experiments. The models, being the current state-of-the-art,
yielded impressive results and achieved high performance.
