Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures

TL;DR

This paper compares Bidirectional LSTM and Transformer-based deep learning models for Named Entity Recognition and Intent Classification in English and Greek, demonstrating their high performance on the ATIS dataset.

Contribution

It provides a comparative analysis of two deep learning architectures for NER and intent classification across two languages, highlighting their effectiveness.

Findings

Both models achieved high accuracy on the ATIS dataset.

Transformer-based networks outperformed LSTM models in most metrics.

The study confirms the effectiveness of deep learning for multilingual NER and intent classification.

Abstract

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.