# Multi-Source Neural Model for Machine Translation of Agglutinative Language
## 6 Conclusions  
In this paper, we proposed a multi-source neural model for the translation task on agglutinative language, which utilizes the source-side linguistic knowledge to enhance the representation ability of the encoder. The model employs two separate encoders to encode the word feature and the linguistic features, respectively. We first extended the input-embedding layer of an encoder to incorporate the linguistic information into NMT. Then, we used a serial combination method to hierarchically integrate the conditional information from the encoders with the outputs of the decoder, which aims to learn a high-quality context representation. The experimental results show that the proposed approach was beneficial to the translation task on the morphologically rich languages, which achieves the highest improvements of +2.4 BLEU points for the Turkish-English translation task and +0.6 BLEU points for the Uyghur-Chinese translation task. In addition, the experimental results show that the proposed multi-source neural model was capable of better exploiting the source-side linguistic knowledge and effectively integrating the linguistic features together. In future work, we plan to utilize other combination methods to further enhance the connection between the encoder and decoder. We also plan to adjust the training parameters to find the optimal conditions for the NMT model on the low-resource and morphologically rich machine translation. Moreover, we plan to use the multi-source framework to perform transfer learning to make better generalizations on the agglutinative languages. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Feature**} & \multicolumn{4}{c}{**BLEU Score**} & \multicolumn{4}{c}{**ChrF3 Score**} \\ \cline{2-9}  & \multicolumn{1}{c}{tst2011} & \multicolumn{1}{c}{tst2012} & \multicolumn{1}{c}{tst2013} & \multicolumn{1}{c}{tst2014} & \multicolumn{1}{c}{tst2011} & \multicolumn{1}{c}{tst2012} & \multicolumn{1}{c}{tst2013} & \multicolumn{1}{c}{tst2014} \\ word + lemma & 24.60 & 25.75 & 26.69 & 25.20 & 47.66 & 48.77 & 49.25 & 48.33 \\ word + POS tag & 24.10 & 25.54 & 26.20 & 24.98 & 47.32 & 48.66 & 48.84 & 47.98 \\ word + morph tag & 24.34 & 25.70 & 26.39 & 25.50 & 47.41 & 48.61 & 49.08 & 48.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experimental results of incorporating a single linguistic feature into the multi-source model. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Turkish-English Translation Samples**} \\ \hline source sentence 1 & Afghanistan amerika’da buradan o kadar farkli görtinüyor ki. \\ reference & Afghanistan looks so different from here in America. \\ NMT baseline model & It looks so different from here in Afghanistan. \\ multi-source model & Afghanistan is so different from here in America. \\ source sentence 2 & Burada bir pijama partisinde, fransiz vogue cekiminden birkac gün once. \\ reference & Here’s me at a slumber party a few days before I shot French Vogue. \\ NMT baseline model & Here’s a pajama party, a few days before the French pullout. \\ multi-source model & Here I had a pajama party, a few days before French shot. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The translation samples for the different NMT models for the Turkish–English translation task. **Author Contributions:** Investigation, Y.P. ; visualization, Y.P. ; writing--original draft, Y.P. ; writing--review and editing, X.L., Y.Y., and R.D. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the High-Level Talents Introduction Project of Xinjiang, grant number Y839031201; the National Natural Science Foundation of China, grant number U1703133; the National Natural Science Foundation of Xinjiang, grant number 2019 BL-0006; the Open Project of Xinjiang Key Laboratory, grant number 2018D04018; and the Youth Innovation Promotion Association of the Chinese Academy of Sciences, grant number 2017472. **Conflicts of Interest:** The authors declare no conflict of interest.