## 2 Related Work  
ADI has been receiving significant attention in recent years. The methodologies used to tackle this intricate problem can be divided into five main categories (see [14] for a survey of the literature): first, nonautomated manual methods that depend on lexicons and linguistic rules; second, language models that estimate the probability of different linguistic units belonging to a particular dialect; third, machine learning; and finally, deep learning models. As this study focuses on solving the ADI problem using classical machine learning and deep learning approaches based on character-level input features, this section will only review the studies that covered these approaches. More recently, a number of works on ADI has involved the use of machine learning (ML) approaches with feature engineering, where the performance of different models using different features is assessed and compared [14]. [PERSON] et al. [15] compiled a dataset from social media outlets encompassing 18 different dialects, in an attempt to provide a framework for the multiclassification of ADI tasks. They implemented a Markov language model and a naive Bayes (NB) classifier using unigram, bigram, and trigram character-level features. The best performance was recorded for the character-level bigram NB, with an F1 score of 80%. [PERSON] et al. [9] performed a fine-grain ADI task covering 25 Arabic dialects plus MSA. They used two datasets. The first dataset consisted of 2000 sentences translated into 25 cities' dialects and MSA (Corpus-26). The second had an additional 10,000 sentences translated into the dialects of five cities and MSA (Corpus-6). A number of different combinations of features were used to train the models, including word n-grams, character n-grams, and language model probability scores. Two machine learning algorithms were applied: linear support vector machine (SVM) and multinomial naive Bayes (MNB), which reported the best-performing model results. The best performance set of features combined character-level uni-, bi-, and trigrams, word unigrams, and the probability score of a five-gram character language model. A study by [PERSON] et al. [16] used an SVM to distinguish between Arabicised Berber and seven ADs at the country level (i.e., Algerian, Egyptian, Gulf, Levantine, Mesopotamian, Moroccan, and Tunisian) plus MSA. The dataset was a manually annotated corpus of blogs and newspapers that they had collected. The best feature set combined character-level five-grams and six-grams, and a lexicon that they constructed, weighted using the term frequency-inverse document frequency (TF-IDF). The SVM using the features mentioned resulted in an F1 score of 92.94%. [PERSON] et al. [17] put forward an ADI problem to distinguish between the transcriptions of the conversational speech of four Arabic dialects (i.e., Egyptian, Gulf, Levantine, and North African) and MSA. [PERSON] et al. [18], in an attempt to solve the ADI task posed by [17], also used a linear SVM. The best performance was achieved using character-level five-gram and six6-gram features (F1 score= 49.5%). Using the same data as in [17], [PERSON] [19] reported the best performing model as an SVM with character-level bi-, tri-, four-, and five-grams together weighted using TF-IDF (accuracy = 51.36%). Using the same features, the second best-performing model was a logistic regression with the same features (accuracy = 50.82%). Another study by [PERSON] et al. [5] conducted a six-way classification ADI task using a linear SVM. They used the dataset compiled by [20] containing a collection of 2000 parallel sentences in five ADs plus MSA for training the model. The results showed that the character n-grams were the best features compared to other features such as word n-grams. The best performance was achieved using a combination of character uni-, bi-, and trigrams with an accuracy of 66.48%.