U o T at NADI 2023 shared task: Automatic A rabic Dialect Identification is Made Possible (2023)

Abstract: In this paper we present our approach towards Arabic Dialect identification which was part of the Fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). We tested several techniques to identify Arabic dialects. We obtained the best result by fine-tuning the pre-trained MARBERTv2 model with a modified training dataset. The training set was expanded by sorting tweets based on dialects, concatenating every two adjacent tweets, and adding them to the original dataset as new tweets. We achieved 82.87 on F1 score and we were at the seventh position among 16 participants.

## introduction arabic dialects are different spoken versions of modern standard arabic (msa) which become to increasingly emerge in a written format recently. although arabic dialects have common linguistic features with msa, they have different features where nlp tools used for msa fail to work properly. there are more than 27 arabic dialects @xcite which need different nlp techniques than those used for msa. it was proven that nlp tools for msa is less efficient with arabic dialects @xcite . as such, it is crucial to identify a dialect version in order to properly apply proper nlp techniques on it. arabic dialect identification is very challenging for several reasons. first, arabic dialects are all originating from msa and share common features and words. as msa is the formal language of writing across arabic countries, writing dialectal phrases are usually mixed with msa complete phrases. furthermore, dialectal arabic has no official spelling standards and usually written differently by different people @xcite . second, with the absence of short vowels (diacritics) in arabic text, it is hard to know the phrase dialect, for example, the word /enti/ (you) in tunisian dialect is used to address both a masculine or a feminine third person, while /enta/ is used to address a masculine and /enti/ to address a feminine third person in several other dialects, while in msa /anta/ and /anti/ are used respectively for the same purpose. third, tweets are usually short and in many cases it is hard not only for a learning model, but for an arabic reader to guess the dialect of the tweet based on its words. previous work on arabic dialect identification were mostly carried out through the nuanced arabic dialect identification (nadi) shared tasks series @xcite @xcite . the goal of these shared tasks is to improve dialect identification and other dialect processing tasks such as sentiment analysis and machine translation from dialects to msa. the organizers provide required resources such as datasets to participants who carry research on those tasks. the forth nuanced arabic dialect identification @xcite has three subtasks: we participated in subtask 1 only. we tested several machine learning and deep learning models which we report in this paper. the remaining part of this paper is structured as follows: section 2 describes the data used in our experiments, section 3 describes our experiments and proposed systems, and in section 4 we present our results proceeded by our discussions and conclusion. ## data for subtask 1, the organizers provided a 23.4k tweets dataset that covers 18 dialects. the dataset is split into 18k training set, 1.8k development set and 3.6k test set. extra datasets was also provided and can be used by participants. particularly, data used in previous nadi competitions plus the madar dataset @xcite . as a closed-country subtask, participants were not allowed to use other external data to train their systems. datasets and their size are presented in ## experiments we run several experiments using both machine learning and deep learning models. we determined our baseline and officially submit the best three outputs of our systems to be scored on the leaderboard. ## discussion dialect identification of a written text is uneasy task. by going through tweets in the development dataset, we found a considerable overlap between regional dialects which is natural, for example gulf dialects usually overlap and are miss judged by language models. for example, saudi-arabian dialect overlaps with qatar, uae, and omani dialects. and maghrebi dialects such as tunisian are falsely judged as algeria and libyan tweets only; and levantine dialects such as syrian are falsely judged as lebanese, jordanian, and palestinian tweets. the best judgement was achieved on moroccan dialect with only 3 tweets judged as tunisian and one as palestinian. false predicted tweets are usually short and are hard for a human to judge. for instance, " " meaning "close the door behind you", is a kuwaiti tweet which is falsely judged as egyptian. this tweet can also be libyan and it is hard to detect its origin dialect. that is why our approach was beneficial in clarifying such tweets. expanding tweets should be explored further. for instance expanding the dataset with a combination of only shorter tweets within the same dialect. we expected that pre-processing would improve identification as it cleans text, however, for dialects it did not. after deep analysis of the training dataset, we realized that removing none arabic characters and normalization should be handled carefully as there are several arabic tweets written in farsi characters which fall out of the range of arabic characters. for example removing charac- ## conclusions we used several machine learning classifiers and pre-trained language models to identify arabic dialects. we also showed the affects of preprocessing, stemming and sotpwords removal on the identification results. our best results are obtained using two pre-trained models namely: the marbertv2 model and the arabert model. we fine-tuned those models with an expanded version of the training dataset. this approach resulted in improving our baseline and put us in the seventh position among 16 participating teams in the fourth nuanced arabic dialect identification shared task.