Work

On The Arabic Dialects’ Identification: Overcoming Challenges of Geographical Similarities Between Arabic dialects and Imbalanced Datasets

Year: 2022

Type: conference-paper

Abstract: Arabic is one of the world's richest languages, with a diverse range of dialects based on geographical origin.In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44%.In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss.On test sets A and B, our model achieved 35.68% and 17.192% Macro F1 scores, respectively.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/2022.wanlp-1.49

Authors: Salma Jamal, Aly M .Kassem, Omar Mohamed, Ali Ashraf

Institutions: Nile University, University of Windsor, Helwan University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Compute Canada, Vector Institute

OpenAlex ID: W4385572949