Hierarchical Deep Learning for Arabic Dialect Identification
Gael de Francony, Victor Guichard, Praveen Kumar Joshi, Haithem Afli, Abdessalam Bouchekif · 2019
In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification.The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification.The main idea is to separate the classification process for a sentence from a given text in two stages.We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes).The second approach is given by a voting system based on Naive Bayes and Random Forest.Our system achieves an F 1 score of 63.02% on the subtask evaluation dataset.