Year: 2017
Type: conference-paper
Abstract: This paper presents the systems submitted by the MAZA team to the Arabic Dialect Identification (ADI) shared task at the VarDial Evaluation Campaign 2017.The goal of the task is to evaluate computational models to identify the dialect of Arabic utterances using both audio and text transcriptions.The ADI shared task dataset included Modern Standard Arabic (MSA) and four Arabic dialects: Egyptian, Gulf, Levantine, and North-African.The three systems submitted by MAZA are based on combinations of multiple machine learning classifiers arranged as (1) voting ensemble; (2) mean probability ensemble; (3) meta-classifier.The best results were obtained by the meta-classifier achieving 71.7% accuracy, ranking second among the six teams which participated in the ADI shared task.
DOI: 10.18653/v1/w17-1222
Authors: Shervin Malmasi, Marcos Zampieri
Institutions: Harvard University, Macquarie University, University of Cologne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2740300618