## 2 Related Work  
Studies have revealed that the existing work on machine translation for Arabic dialects is limited. Most of the recent research work has focused on the statistical and rule-based approaches. [PERSON] [4] introduced a general method to convert sentences from the Egyptian Dialect into vocalized renditions of MSA sentences. To automatically tokenize and tag Arabic sentences, the researchers used the statistical approach. A method specifically based on significant rules was adopted with the aim of creating diacritics for target sentences in Modern Standard Arabic. The work was evaluated on a dataset of 1K of Egyptian dialect sentences (including training and test 800 and 200, respectively). For converting dialect words to MSA words, the system achieved an accuracy of 88%, whereas for producing the words into its correct order, the system performed at an accuracy of 78%. On the other hand, [PERSON] [5] presented PADIC, a multi-dialect Arabic corpus that covers the Modern Stranded Arabic, the Maghrebi dialects (Tunisian and Algerian) and the Levantine dialects (Syrian and Palestinian). In contrary to the recent works in the area, some experiments were conducted on several statistical machine translation systems that ran through a variety of possible pairs of languages (Modern Standard Arabic and dialects). The authors investigated the importance of using the proposed language model on machine translation, by employing smoothing techniques along with including the techniques within a broader framework. The study achieved satisfactory results when translating among the various dialects within Algeria, primarily due to the shared vocabulary. It was remarked that the statistical machine translation performed significantly well when the translation was carried out on the Palestinian and the Syrian dialects. The ease of practicality and effectiveness can be accorded to the linguistic proximity of the two vernaculars, concerning translations into Modern Standard Arabic, remarkable results were obtained with the Palestinian dialects. The rule-based approach was proposed by [PERSON] [6] to transform the Sanaani dialect to Modern Standard Arabic, which is sued in the capital city of Yemen. The system designed generated 77.32% of accuracy on being tested on the Sanaani corpus of 9386 words. [PERSON] & [PERSON] [7] suggested a rule-based approach that relies on the language models to translate the Moroccan dialect to MSA. The method based on the morphological analysis through the use of the Alkhalil morphological analyzer was proposed. [PERSON] & [PERSON] [8] presented Elissa, which is a translation system constructed by rules designed and developed for the conversion of Arabic vernaculars to the standard form of Arabic. The described system works with Levantine (Jordanian, Syrian and Palestinian), Egyptian, Iraqi and Gulf Arabic dialects. [PERSON] [9] presented a model for the translation of the Tunisian Arabic Dialect to the standardized modern form of Arabic. The model is based on a bilingual lexicon which was designed for the particular context of the translation exercise and uses a set of grammatical mapping rules with an additional step for disambiguation. The grammatical mapping rules are based on a language model of Modern Standard Arabic which would aid in choosing the best possible translated target phrase. And, it is a word-based translation system. The model secured a BLEU score [10] of 14.32, in a test set consisting of 50 sentences from the Tunisian dialect. Most of the methods mentioned above focused on the statistical and rule-based approaches. The Rule-Based Machine translation system have a significant drawback: the construction of such systems demands a considerable amount of time. To improve the quality of the Rule-Based Machine Translation RBMT, it is necessary to modify the rules, which requires elevated level of linguistic knowledge.