## 1 Introduction
These profound differences pose challenges to SMT because as the sentences get longer, it is no longer simple enough to contain a subject, object and a verb but are complex constructions made up of several sentential components. Likewise, another challenge on developing a neural machine translation model for Arabic dialect to MSA is the absence of the standardized orthographies for all Arabic dialects. The absence includes morphological differences which are evident in the use of clitics and affixes that do not exist in Modern Standard Arabic. Training NMT models usually require a large amount of annotated data, which is often unavailable in the case of low-resource languages such as Arabic dialects. Furthermore, the quality of translation decreases in parallel to the reduction in the amount of training data for low-resource languages. One of the main issues for developing AD-to-MSA neural machine translation system is the rareness of training data. Arabic Dialects is considered among the languages that has limited access and availability of resources. In the event of sparse parallel data, exploiting other knowledge resources such as POS tags can be crucial for the performance of translation from AD to MSA. The plausible techniques that address Dialectal Arabic machine translation challenges are still under study and no previous research work has focused on employing POS tags as additional linguistic features into NMT model for Arabic dialects. The goal of the current research work is to present a Bi-LSTM-CRF segment-level Arabic Dialect POS tagger model that can be integrated into the NMT model to achieve better performance. In order to carry out the integration, a unified Multitask NMT that trains several machine translations and POS tasks were proposed and developed. In the proposed model, the AD encoder is shared between AD-MSA translation task and the segment-level Bi-LSTM-Conditional Random Filed (CRF) POS tagging task. In addition to, only a shared layer and an invariant layer are shared between the translation tasks. The current study will make use of sequence-to-sequence model for all the tasks. Experiments reveal that the performance on the Levantine Arabic (LA) to Modern Standard Arabic (MSA) machine translation task and the Maghrebi Arabic (MA) to MSA machine translation task measured on the BLEU score can be improved. A high-test accuracy on the POS tagging task for both Levantine Arabic and Maghrebi Arabic was also obtained. Furthermore, the study analyzed three crucial points while designing the proposed model. First, the study investigated the influence of segment-level POS tagging task on Arabic dialect translation. Along with it, the impact of training the models without the POS tagging task with different pre-trained word embedding and with different dimensions was also examined. Lastly, the influence of training the proposed model with FastText pre-trained word embedding with segment-level POS tagging task was analyzed. The research experiments revealed the effectiveness of the chosen approach and pointed out that the approach was effective in leveraging common information to enhance the performance and quality of Arabic dialects translation, exclusively.