## 7 Experiments and Results
### Results  
#### 7.3.1 Automatic Metric  
On conducting the experiment, it can be asserted that the researchers have carried out extensive experiments on the Multitask NMT model by utilizing POS and by not exploiting the POS linguistic resources. The model was also experimented using different pre-trained word embedding like polyglot and FastText [40; 41]. The translation performances measured will be listed in the BLEU score automatic evaluation metric [10]. The results in Table 1 show the efficiency of Multitask NMT model without adding POS tagging using different pre-trained word embedding. It can be noticed from Table 1 that; the proposed model has obtained excellent results with the FastText pre-trained embedding for both LA-MSA and MA-MSA translations tasks. The results suggest success and satisfaction because the FastText pre-trained word embedding was trained on the Egyptian dialects.  
Moreover, the FastText vectors in dimension 300 were obtained using the Skip-Gram model. Therefore, it can be summarized that the models that were trained using the FastText pre-trained word embedding outperforms the models that were trained using polyglot. It also outperforms the model that was trained using random embedding. It is clear from the Table 2 that in all cases, the proposed Multitask NMT model with POS tagging outperforms the Multitask NMT model (that is trained without POS tagging). More importantly, results in Table 2 shows that the performance of Multitask NMT model that exploits the POS tagging for the LA-MSA, MA-MSA and ENG-MSA task is higher when compared to the Multitask learning- based Arabic dialect NMT model, which was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pairs** & **Embedding Type** & **Embedding Size** & **Hidden Size** & **Epochs** & **BLEU** \\ \hline LA-MSA & Random & 150 & 150 & 102 & 0.36 \\ MA-MSA & Random & 150 & 150 & 102 & 0.26 \\ MSA-ENG & Random & 150 & 150 & 49 & 0.29 \\ ENG-GER & Random & 150 & 150 & 45 & 0.34 \\ LA-MSA & FastText & 300 & 150 & 52 & 0.33 \\ MA-MSA & FastText & 300 & 150 & 52 & 0.32 \\ LA-MSA & Polyglot & 64 & 150 & 64 & 0.26 \\ MA-MSA & Polyglot & 64 & 150 & 64 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Multi-Task neural machine translation (NMT) without part-of-speech (POS) in terms of BLEU score.  
proposed by [PERSON] et al. [11] as shown in Table 3. The result produces a definite understanding because the closeness of Levantine Arabic and Modern standard Arabic and both dialects share many vocabularies. The central reason behind the development of the result was the utilization of the segment- level POS tagging RNN-CRF model and the utilization of the shared-private scheme for both the dialects. Exploiting the POS tags for both dialects (Levantine and Maghrebi) as an additional feature in Multi-task NMT model was beneficial in word ordering between languages and produced better translation quality. By utilizing the shared private scheme, the model was able to learn better representation and capture more features from source languages and handle the syntactic issue in Arabic dialect which is the word ordering problem. Also, it was noticed that Maghrebi Arabic which is a mixture of many different languages (Berber, Latin, (African Romance) old Arabic, Turkish, Spanish, Italian and Niger-Congo languages) and new word from English and French; the Multitask NMT model improved the performance of translation on the training dataset of Maghrebi Arabic-MSA. Moreover, the outstanding results on BLEU score were an inherent result of the inductive transfer, which can be utilized to leverage the additional source of information to improve generalization accuracy, the speed of learning and the intelligibility of learned models. Furthermore, the model has improved the performance of learning on the MSA-English and English-German translation tasks.