## 7 Conclusions and Future Work  
In order to improve the translation quality of MN-CH NMT on a low-resource corpus, we explored a way to enhance the syntactic learning ability of the attention mechanism. We designed a translation model with double-ended learning syntactic knowledge. A syntax-assisted learning unit \(M\) was added to the self-attention layer of each sublayer of the encoder for implicitly enhancing the encoder's ability to capture syntactic information in Traditional Mongolian. MMHDPA was added before the first sublayer of the decoder, and Chinese dependent syntactic knowledge was added to the attention mechanism of MMHDPA for explicitly learning Chinese syntactic knowledge. The experiments show that Transformer_Enc_Dec_Imp and Transformer_Enc_Dec_Imp + Syn both had higher average BLEU scores than the baseline model under the same training conditions, and Transformer_Enc_Dec_Imp + Syn outperformed Transformer_Enc_Dec_Imp, which suggests the proposed improved model structure can effectively improve the translation quality even in the absence of external knowledge. The improvement was more obvious with the addition of external knowledge. Both the proposed model structure and external syntactic knowledge can improve the quality of translation. The analysis revealed that the decoder side suffered from an insufficient fitting ability and MMHDPA did not sufficiently learn Chinese dependent syntactic information. Therefore, the Primer-EZ method was incorporated into the existing model. In Transformer_Base and Transformer_Enc_Imp, only their activation functions were replaced with Squared Leaky ReLU. In Transformer_Dec_Imp + Syn, Transformer_Enc_Dec_Imp and Transformer_Enc_Dec_Imp + Syn both used Squared Leaky ReLU as the activation function and also added a shared convolution layer after the \(Q\), \(K\), and \(V\) of the MMHDPA layer to enhance local feature capture. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **BLEU** & **Training Set** & **Validation Set** & **Test Set** &
\begin{tabular}{c} **Max. Sentence** \\ **Length** \\ \end{tabular} \\ \hline [PERSON] et al. [31] & 21.09 & 58,900 & 1000 & 1000 & - \\ [PERSON] et al. [34] & 31.18 & 63,000 & 1000 & 1000 & 50 \\ [PERSON] et al. [35] & 19.4 & 421 & 52 & 52 & - \\ Our Approach & 45.634 & 479,163 & 9979 & 9779 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of different Traditional Mongolian-Chinese machine translation approaches on various datasets. The final improved model, Transformer_Enc_Dec_Imp + Syn + EZ (Both), showed a significant improvement of 9.113 (45.634 - 36.521 = 9.113) in the average BLEU value compared with the baseline model for experimental conditions of N = 5 and epochs = 35. The conclusion can be drawn that Squared Leaky ReLU can accelerate the convergence speed of the model, and a CNN can significantly enhance the local feature capture of Chinese syntax. Both improvements can improve the quality of Mongolian-Chinese NMT translations. Overall, our proposed method was validated in the MN-CH parallel corpus and achieved decent results. For future work, firstly, other ways of constructing dependent syntactic matrices were not explored in this paper due to time constraints, and it is hoped that more constructions will be attempted. Secondly, an improved encoder self-attention mechanism could be used to perform grammar induction tasks for small languages such as Mongolian. Grammar induction refers to the model automatically learning grammatical structures from the corpus without any expert annotated syntactic knowledge. One possible solution is that the self-attention mechanism looks at the relationships between principles from a global perspective by strengthening the relationships between principles and using this to delineate the structure of sentences, and Transformer has several encoder sublayers that can be used to delineate the hierarchical structure of sentences. Conceptualization, R.Q.-d.-e.-j. ; methodology, R.Q.-d.-e.-j. and K.C. ; software, K.C. ; validation, K.C.