IUNADI at NADI 2023 shared task: Country-level Arabic Dialect Classification in Tweets for the Shared Task NADI 2023
Year: 2023
Type: conference-paper
Abstract: In this paper, we describe our participation in the NADI2023 shared task for the classification of Arabic dialects in tweets.For training, evaluation, and testing purposes, a primary dataset comprising tweets from 18 Arab countries is provided, along with three older datasets.The main objective is to develop a model capable of classifying tweets from these 18 countries.We outline our approach, which leverages various machine learning models.Our experiments demonstrate that large language models, particularly Arabertv2-Large, Arabertv2-Base, and CAMeLBERT-Mix DID MADAR, consistently outperform traditional methods such as SVM, XGBOOST, Multinomial Naive Bayes, AdaBoost, and Random Forests.
Authors: Yash Hatekar, Muhammad Abdo
Institution: Indiana University Bloomington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: Eli Lilly and Company, Lilly Endowment
OpenAlex ID: W4389518394