D ialect NLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in A rabic (2023)

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

## introduction the arabic language, with approximately 400 million speakers across the globe, stands as the fifth most widely spoken language worldwide (mohammed @xcite . its vast linguistic diversity, rooted in rich historical and regional variations, necessitates continuous advancements in the field of natural language processing (nlp) @xcite . within the scope of this linguistic diversity, modern standard arabic (msa) serves as the standardized form of the language, fostering communication across arabic-speaking regions. however, coexisting with msa are numerous dialects, each bearing its unique linguistic features and nuances @xcite . arabic encompasses a wide array of languages and language variations, with some of them lacking mutual intelligibility @xcite ) @xcite . despite this diversity, arabic is frequently misconceived as a single, uniform language. thus, identifying these different dialects plays a pivotal role in the realm of arabic language understanding, primarily due to the contextual intricacies they introduce @xcite . dialect identification serves as the bedrock for a multitude of nlp applications, enabling accurate language understanding, effective communication, sentiment analysis, and sociolinguistic insights @xcite . furthermore, dialect classification preserves and celebrates the rich linguistic diversity encapsulated within the arabic language landscape @xcite . similarly, machine translation, in particular, holds profound significance within this rich arabic linguistic landscape @xcite . bridging the gap between dialects and the standardized form of the language, msa, and machine translation facilitates seamless communication across arabic-speaking communities @xcite . in an interconnected world where communication knows no borders, machine translation becomes the vital bridge that transcends linguistic differences @xcite . in this paper, we address the pressing need for advancements in arabic dialect identification and machine translation. specifically, we present our contributions to the nuanced arabic dialect identification (nadi) task @xcite at 1st arabicnlp colacted with emnlp 2023. our work centers on two crucial subtasks: this paper explains our systems in detail, offering comprehensive insights into our approach, the rationale behind our methodology, a thorough analysis of our results, and valuable insights derived from our findings. ## task descriptions we submitted results for the first two out of three subtasks. subtask 1: this subtask involves identifying the dialect of a given text, with a particular emphasis on arabic dialects that lack well-defined linguistic conventions and structures. the evaluation metric for this subtask is the macro-averaged f1-score. subtask 2: this subtask entails translating non-msa dialects into modern standard arabic (msa) across four specified dialects. evaluation is based on the bleu score. ## results to provide deeper insights into our models' performance, figure 1 presents confusion matrices for all 18 dialects in subtask 1. these matrices offer a detailed breakdown of classification results, shedding light on how well our models identify each dialect. notably, sudanese and moroccan dialects exhibit a strong classification, while some challenges persist in accurately classifying palestinian and kuwaiti dialects. the confusion matrix serves as a valuable tool for understanding model performance in specific dialects and identifying areas for improvement, further enriching our analysis of dialect classification results. similarly, in subtask 1, focused on dialect identification and subtask 2, addressing machine translation, ensemble techniques (fusion-level or decision-level) have consistently demonstrated outstanding performance. by strategically combining multiple models, we have harnessed the collective strengths of various standalone models to achieve remarkable results. this underscores the pivotal role of ensemble methodologies in enhancing the accuracy and robustness of arabic dialect identification and machine translation, reaffirming their effectiveness across diverse linguistic challenges. ## conclusion in conclusion, our participation in the nuanced arabic dialect identification (nadi) task at emnlp 2023 has demonstrated the effectiveness of innovative approaches in addressing the intricate challenges posed by arabic dialect identification and machine translation. with its diverse linguistic landscape, arabic presents a unique and formidable set of hurdles for natural language processing tasks. the high performance of ensemble strategies that involve carefully combining various models has showcased remarkable achievements in dialect classification and machine translation, underlining the power of ensemble techniques. furthermore, our contributions extend beyond performance metrics, encompassing comprehensive system descriptions, model rationale, and insights from experimentation. these approaches pave the way for tackling the multi-aspects challenges of arabic nlp forward.