An Attention Based Neural Network for Code Switching Detection: English & Roman Urdu

2021/03/03 by Aizaz Hussain, Hussain, Aizaz, Muhammad Umair Arshad +1
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Communication and Language #FOS: Computer and information sciences #Multilingual Education and Policy

paper · pdf · doi:10.48550/arxiv.2103.02252

openalex publication_date 2021/03/03 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28

Abstract

Code-switching is a common phenomenon among people with diverse lingual background and is widely used on the internet for communication purposes. In this paper, we present a Recurrent Neural Network combined with the Attention Model for Language Identification in Code-Switched Data in English and low resource Roman Urdu. The attention model enables the architecture to learn the important features of the languages hence classifying the code switched data. We demonstrated our approach by comparing the results with state of the art models i.e. Hidden Markov Models, Conditional Random Field and Bidirectional LSTM. The models evaluation, using confusion matrix metrics, showed that the attention mechanism provides improved the precision and accuracy as compared to the other models.

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