DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic

Abstract

With approximately 400 million speakers worldwide, Arabic ranks as the fifth mostspoken language globally, necessitating advancements in natural language processing. This paper describes the approaches employed for the subtasks outlined in the Nuanced Arabic Dialect Identification (NADI) task at EMNLP 2023. We employ an ensemble of two Arabic language models for the first subtask involving closed country-level dialect identification classification. Similarly, for the second subtask, focused on closed dialect to Modern Standard Arabic (MSA) machine translation, our approach combines sequence-to-sequence models trained on an Arabic-specific dataset. Our team ranks 10th and 3rd on subtask 1 and subtask 2, respectively.