# A Mongolian-Chinese Neural Machine Translation Method Based on Semantic-Context Data Augmentation
## 5 Conclusions  
In the context of the robust evolution of data augmentation methods in NMT, this study explores the data augmentation challenges within MNMT. Aiming to solve the problem of data sparseness and semantic inconsistency in MNMT, this paper proposes an MNMT method based on semantic-context DA. This method adds an additional semantic encoder to the Transformer translation model to extract the semantic information of the source and target languages. This study maps Mongolian and Chinese into a shared semantic space by semantic encoding, and applies comparative learning to optimize the space that contains rich semantic vector variants. Subsequently, a semantic vector is sampled from this space, fused with the vector generated by the encoder, and then fed into the decoder for translation. This DA method can use the semantic information of both the source and target to improve translation performance during the training process. Compared with the baseline model, the data augmentation method proposed in this paper significantly improved the translation results, especially in the translation of low-frequency words such as place names and names, and its effectiveness has also been verified in other low-resource datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Mn-Ch** & **Vi-Ch** & **Ti-Ch** \\ \hline Transformer & 30.26 & 31.4 & 19.98 \\ \hline SwitchOut & 31.32 & 29.38 & 20.34 \\ \hline SemAug & 30.78 & 29.14 & 20.51 \\ \hline SCA & 31.02 & 28.77 & 20.87 \\ \hline Sem-NMT (ours) & 32.73 & 32.15 & 21.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Translation results of Sem-NMT on different datasets.  
Figure 7: Changes in loss and accuracy during the translation process.  
While the use of semantic-context data augmentation methods to generate diverse training samples alleviates the issue of resource scarcity in MNMT, there still exists a discernible gap between the generated samples and the original training data. In future endeavors, we aim to further explore methods to optimize data augmentation techniques, striving to enhance the quality of MNMT, specifically focusing on the issues of grammar and semantic alignment between Mongolian and Chinese.  
Conceptualization, N.W. and M.L.; Methodology, H.Z., Y.J. and N.W.; Validation, H.Z.; Resources, N.W.; Writing--original draft, H.Z.; Writing--review & editing, Y.J. and M.L.; Supervision, Y.J. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 62066035, 62206138 and Universities Directly Under the Autonomous Region Funded by the Fundamental Research Fund Project grant number JY20220122, JY20220089, RZ2300001739, RZ2300001743, JY20220186 and Research program of science and technology at Universities of Inner Mongolia Autonomous Region grant number NJZZZZ2251, NJJZZ23081 and Science Research Foundation of Inner Mongolia University of Technology grant number BS2021079, ZZZ02118, DC230001261, DC2300001258, DC2300001262. The APC was funded by Universities Directly Under the Autonomous Region Funded by the Fundamental Research Fund Project grant number JY20220186.  
Restrictions apply to the availability of these data. Data were obtained from the Artificial Intelligence Laboratory, School of Information Engineering, Inner Mongolia University of Technology, and are available from the authors with permission from the Artificial Intelligence Laboratory, School of Information Engineering, Inner Mongolia University of Technology.  
The authors declare no conflicts of interest.