NLP eople at NADI 2023 Shared Task: A rabic Dialect Identification with Augmented Context and Multi-Stage Tuning (2023)

Abstract: This paper presents the approach of the NLPeople team to the Nuanced Arabic Dialect Identification (NADI) 2023 shared task. Subtask 1 involves identifying the dialect of a source text at the country level. Our approach to Subtask 1 makes use of language-specific language models, a clustering and retrieval method to provide additional context to a target sentence, a fine-tuning strategy which makes use of the provided data from the 2020 and 2021 shared tasks, and finally, ensembling over the predictions of multiple models. Our submission achieves a macro-averaged F1 score of 87.27, ranking 1st among the other participants in the task.

## introduction the task of dialect identification involves predicting the source variety of a given text or speech segment. recently, there have been a number of shared tasks that have focused on predicting the nuanced dialects of arabic @xcite @xcite . arabic can be broadly categorised into the following three languages: classical arabic (ca), modern standard arabic (msa), and dialectal arabic (da), where da can be further sub-categorised based on the geographic region where it is spoken. arabic dialect identification represents a challenging task for a number of reasons. firstly, arabic languages exhibit rich morphology, where words are highly-inflected, which can lead to issues related to data sparsity. another challenge present in the nadi shared tasks, is that the text to be classified consists of tweets, a form of user-generated content (ugc). as pointed out by @xcite , ugc contains features not typically found in other forms of text data such as spoken language and standardised written language. for instance, ugc in the form of tweets tend to be short, exhibit non-standard use of grammar, and contain increased usage of emojis and abbreviated text. this paper describes the nlpeople submission to the 2023 nadi shared task @xcite . in order to deal with the challenge of arabic dialect identification, we develop a system which makes use of the following components: we empirically show that each of these components improves upon the metric of macro-averaged f1 score over the included dialects. overall, our results rank 1st among 16 participants with a macroaveraged f1 score of 87.27. ## dataset the label distribution of the used datasets are given in figure 1 . for the nadi-2023 data, a total of 18 country-level labels are present, and the training and development data have an equal distribution of 1000 and 100 labels, respectively. additionally, we include the nadi-2020 and nadi-2021 datasets that were released by the shared task organisers as additional data for training our models. these datasets exhibit an imbalanced label distribution compared to the nadi-2023 data, with the uae label being absent, and certain dialects such as bahranian and qatari being less represented than dialects such as egyptian and saudi arabian. the total number of unlabelled instances in the 2023 test set is 3600. 2023, 2021 and 2020 training data. 3 system description