## 2 Related Work
Moreover, the statistical approaches need high computational resources and cannot handle one of the syntactic issues in Arabic dialects; which is the word ordering problem. Multitasking is considered to be an effective methodology in improving the performance of an Arabic dialect translation task, with the help of other NLP related tasks such as the POS tagging. The only recent research work that has focused on neural machine translation for Arabic dialect was proposed by [PERSON] et al., [11]. The researchers introduced a multi-task NMT model to translate from AD to MSA. The model is based on multi-task learning (MTL), which shares one decoder among language pairs and each source language has a separate encoder. Experiments show that, given a small parallel training data, the multi-task neural machine translation model is sufficient to generate the correct sequence and produce translations of high quality while learning the predictive structure of multiple targets. Amongst the several approaches for the translation of Arabic dialect, external information into neural network-based models for the English language is considered as one of the most relevant. [PERSON] [12] proposed a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition and semantic role labelling. The basic multi-task architecture of the model is carrying out the sharing of the lower layers to determine the characteristic features. After the shared layers, the remaining layers are split into the multiple specific tasks. Instead of exploiting man-made input features carefully optimized for each work, the model learns internal representations by vast amounts of mostly unlabeled training data. Additionally, the CNN (Convolutional Neural Network) model was used in the work. [PERSON] [13] presented a multitask learning to learn across multiple tasks jointly. By using the recurrent neural network, three different configurations of information sharing were applied to the model text, with task-specific and shared layers. The whole network is jointly trained on all the tasks. The experiments on four benchmark text classification tasks revealed that the introduced models can improve the performance of a task with the help of other related tasks. There have been few papers published on translating texts between closely-related languages. One example is the work by [PERSON] [14] which showed a comparison between rule-based system, phrase-based system and neural machine translation (MT) systems in the Catalan-Spanish language pairs. When using in-domain test set, the performance of neural MT is better than other approaches. The experiments on the out-of-domain test set revealed that the rule-based system (Spanish-to-Catalan, in BLEU) and the phrase-based system (Catalan-to-Spanish) achieved better performance [PERSON] [15] proposed and implemented an Intralingual Machine Translation for translating texts in Kurmani to Sorani. The author used word-for-word translation (literal or direct translation) among the dialects. The results were tested using human raters method. According to different human raters, the experiments showed that this naive approach could provide a significantly intelligible results. The experiments also showed that this approach is might be able to be considered as an immediate remedy for the current lack of corpus issue. [PERSON] et al. [16] introduced the first NMT system trained to translate between national language varieties. The researchers used the language variety pair Brazilian and European Portuguese as an example and parallel corpus of subtitles to train the NMT system. Compared to a Statistical Machine Translation (SMT) system trained on the same data, they reported a performance of 0.9 BLEU points in translating from European Portuguese to Brazilian Portuguese and 0.2 BLEU points when translating in the opposite direction.