## 6 Proposed Model  
The proposed multitask NMT model relies on recent machine translation architectures for multiple languages [26; 27; 28]. The overall structure of multitask NMT model consist of four parts as shown in Figure 2, shared encoder E, decoder D, shared-private layer PL, shared-language invariant layer IL and conditional random field layer (CRF). The model is designed to carry out the translation tasks and POS tagging task in parallel. The first task is the translation from Arabic Dialect (AD) to the Modern Standard Arabic (MSA), the second task is the translation from the Modern Standard Arabic (MSA) to English (ENG) and the POS tagging task for Arabic Dialect. Therefore, all parts (two encoders, one shared private layer, one shared language invariant layer, two decoders and a conditional random field layer (CRF)) are applicable to all the tasks in general. Hence, there are seven components E1_AD, E1_Segment, E2_MSA, PL_AD, PL_MSA, IL_AD, IL_MSA, D1_MSA, D2_ENG and CRF_POS. The key factor relevant for designing the Multitask learning model is the level of sharing across of the tasks. The Multitask NMT model is able is to learn the translations problem together with other related issues, such as POS tagging at the same time by using a shared representation. In the case of task commonality and limited training data, model shows a better performance than a model singularly trained on a single dataset. Moreover, the model can capture more morphological, semantic, syntactic and lexical features of Arabic dialects. Part of speech tagging (POS) is one of the main building blocks in many natural language processing applications. The POS of Arabic dialects is challenging due to the lack of spelling standards and the pervasiveness of transformative morphological operations suchas letters substitution or deletion and word merging and lexical borrowing from foreign languages. Research work on Arabic dialects POS tagging has focused on developing resources and tools for each dialect separately [29; 30], because each dialect has different affixes, different lexical and word ordering choices and many foreign languages influence each dialect. Word segmentation and POS tagging are core steps such as the neural machine translation, for high-level natural language processing tasks. As a reason for exploiting external linguistic features in proposed Multitask NMT model, the current study presents a segment-level part of speech tagger for Arabic Dialect using Bi-LSTM-CRF architecture. The POS dialectal Arabic dataset described by [PERSON] [31] was used. Segmentation and POS tagging, as proposed by [PERSON] [32], to overcome the need for the standardization of different dialectal writings, were applied to the original raw text without any modification and correction. For example, the word (baeheb asma) \"I love to listen\" is segmented as (ba + heb + asma) and tagged as PROG PART+V-V. Words are segmented into tokens (clitits) and POS is given for each token. In the dataset, tokens, words and sentences are separated by token boundary tag (TB), word boundary tag (WB) and end of sentence tag (EOS) respectively. Boundaries are potentially useful, as it can be used to learn the symbols to be attended to and when to forget the information in the recurrent layer. Using the Arabic Dialects segments as an input feature to the shared encoder between the AD-MSA and POS tasks, it guarantees the process of information sharing between the word forms and the segment form. Neural models can learn that inflectional variants are semantically related and can be represented as a similar point in the vector space. The researchers believe that the segment representation increases the data efficiency; low-frequency word or variants might be unknown to the word level model. By sharing the encoder between the segment-level POS tagging task and AD-MSA translation task, the model will learn the similarity between Arabic Dialect words that share the same segments and capture more syntactic and semantic features.