Multilingual Name Entity Recognition and Intent Classification employing Deep Learning architectures

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