###### Abstract  
Neural machine translation (NMT) is a data-driven machine translation approach that has proven its superiority in large corpora, but it still has much room for improvement when the corpus resources are not abundant. This work aims to improve the translation quality of Traditional Mongolian-Chinese (MN-CH). First, the baseline model is constructed based on the Transformer model, and then two different syntax-assisted learning units are added to the encoder and decoder. Finally, the encoder's ability to learn Traditional Mongolian syntax is implicitly strengthened, and the knowledge of Chinese-dependent syntax is taken as prior knowledge to explicitly guide the decoder to learn Chinese syntax. The average BLEU values measured under two experimental conditions showed that the proposed improved model improved by 6.706 (45.141-38.435) and 5.409 (41.930-36.521) compared with the baseline model. The analysis of the experimental results also revealed that the proposed improved model was still deficient in learning Chinese syntax, and then the Primer-EZ method was introduced to ameliorate this problem, leading to faster convergence and better translation quality. The final improved model had an average BLEU value increase of 9.113 (45.634-36.521) compared with the baseline model at experimental conditions of N = 5 and epochs = 35. The experiments showed that both the proposed model architecture and prior knowledge could effectively lead to an increase in BLEU value, and the addition of syntactic-assisted learning units not only corrected the initial association but also alleviated the long-term dependence between words. attention mechanism; dependency parsing; convolutional neural networks; Transformer +
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In 2017, Google Brain proposed the Transformer model [14], which is entirely based on the attention mechanism, and it has quickly become the benchmark model due to its excellent performance. Variants based on the Transformer model have proliferated and become important models for various tasks in the field of NLP [15; 16; 17]. In the self-attention mechanism, \(QK^{T}\) represents the degree of association between any two words in the sequence, and the larger the dot product of two word vectors, the stronger the association between the corresponding two words. However, the Transformer model is also subject to the same constraints as other NMT models: computer arithmetic power, model structure, and the amount of corpus resources. The level of computer hardware has reached a certain height, and some effective structures and tricks have also been proposed. The establishment of the corpus requires a lot of manual effort and time, which is a major problem that limits the translation quality of NMT. Therefore, NMT translation in low-resource corpus needs more research and attention. Prior knowledge has been proven to improve the quality of machine translation [18; 19; 20; 21; 22], which is indeed a worthy research direction for small language machine translation. NMT assumes that syntactic knowledge can be learned automatically during training [11]. Studies have shown that this level of learning is still not enough to capture deep syntactic details, and additional syntactic information can improve the translation performance [23; 24; 25; 26; 27]. Syntactic structure arranges words into a meaningful sentence, and incorporating syntactic knowledge into the NMT model helps to eliminate ambiguity. Syntactic analysis is generally divided into constituent syntactic parsing and dependency syntactic parsing. Constituent syntactic parsing is used to determine the lexical components and phrase structure of a sentence, and dependency syntactic parsing is used to identify the dependency relations between words. Since the self-attention mechanism is concerned with associations between words, the dependency syntactic relations were chosen as the external linguistic knowledge in this paper. In fact, there have been some attempts to improve the Transformer model's attention mechanism. [PERSON] et al.