# Multi-Source Neural Model for Machine Translation of Agglutinative Language
## 5 Results and Discussion  
The experimental results for the Turkish-English and Uyghur-Chinese machine translation tasks are shown in Tables 5 and 6, respectively. For the Turkish-English machine translation task, from Table 5 we can see that the multi-source neural model outperformed both the NMT baseline model and the single-source neural model. It achieved the highest BLEU scores and ChrF3 scores on all the test datasets. Moreover, it achieved the highest improvements on the tst2014 dataset of 2.4 BLEU points (24.98\(\rightarrow\)27.37) and 1.6 ChrF3 points (48.05\(\rightarrow\)49.74). For the Uyghur-Chinese machine translation task, from Table 6 we can see that the multi-source neural model also outperformed both the NMT baseline model and single-source neural model, achieving an improvement of 0.6 BLEU points (27.60\(\rightarrow\)28.21) and 0.7 ChrF3 points (36.73\(\rightarrow\)37.44) on the test dataset. The experimental results show that the proposed approach is capable of effectively improving the translation performance for agglutinative languages. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Encoder**} & \multirow{2}{*}{**Linguistic Feature**} & \multicolumn{2}{c}{**Vocabulary Size**} & \multicolumn{2}{c}{**Embedding Size**} \\ \cline{3-6}  & & **Turkish** & **Uyghur** & **Turkish** & **Uyghur** \\ \hline word-based encoder & word & 32,064 & 32,328 & 512 & 512 \\  & lemma & 30,637 & 31,830 & 352 & 352 \\  & POS tag & 14 & 16 & 64 & 64 \\  & morph tag & 9176 & 6057 & 96 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The vocabulary and embedding sizes for the source-side Turkish and Uyghur translation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Neural Translation Model**} & \multicolumn{4}{c}{**BLEU Score**} & \multicolumn{4}{c}{**ChrF3 Score**} \\ \cline{2-7}  & tst2011 & tst2012 & tst2013 & tst2014 & tst2011 & tst2012 & tst2013 & tst2014 \\ NMT baseline [12] & 24.18 & 25.95 & 26.60 & 24.98 & 47.18 & 48.61 & 48.65 & 48.05 \\ single-source model [18] & 24.69 & 26.65 & 27.43 & 25.98 & 47.99 & 49.26 & 49.89 & 48.87 \\ multi-source model & 25.44 & 26.75 & 28.48 & 27.37 & 48.30 & 49.80 & 50.61 & 49.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The experimental results on Turkish-English machine translation task. In addition, we found that the performance improvements of both the multi-source neural model and single-source neural model for the Uyghur-Chinese machine translation task were not very obvious. The main reason was that the morphological analysis tool for the Uyghur word annotation was not accurate enough. Since the complex morphology of Uyghur, many words cannot be effectively identified and classified, thus the tool simply annotates all the unknown words with a uniform token \"<unk>\" as the same with their POS tags, which increases the complexity for model training. The experimental results indicate that the annotation quality of the source-side sentence makes a difference on the translation quality of the NMT model with linguistic input features. Figure 4 shows the loss values as a function of the time steps (saved every 100 steps) on the validation dataset in different neural translation models for the Turkish-English machine translation task. We can see that in spite of using two encoders, which leads to more model parameters and training time, the loss values of our proposed multi-source neural model is still consistent with the NMT baseline model and the single-source model. This fact indicates that our model was robust and feasible for the agglutinative language translation task. The loss value converges continuously until it achieves a lower value, then it oscillates in a small interval. Thus, we stop the NMT model training process after 10,000 steps without obvious reductions on the loss value.