## 3 System Design
### Multi Language Neural Machine Translation Model Based on BART
The implementation of BART for machine translation tasks involves fine-tuning the introduced decoding adaptor and encoding adaptor. This not only circumvents extensive tuning of the BART, but also sustains BART's high-precision performance in natural language generation tasks. Since parallel corpora of low-resource languages are data-poor, a pivot language transformation approach was employed to augment and expand the corpus with data [40]. The process for enhancing data with the pivot language transformation method [41; 42; 43] is shown in Figure 4.  
However, in low-resource situations, the initial base translation model is obtained by training from a small-scale parallel corpus. As a result, pseudo-parallel sentence pairs that contain more noise may be generated, possibly leading to insignificant or limited improvements in the final translation outcomes. To address the issue of low-quality pseudo-parallel corpus, a filtering mechanism is proposed by first using the real parallel corpus to train the reverse neural machine translation models, and then feeding the monolingual corpus of the respective models into the reverse machine translation model to generate translations. Finally, the filtering mechanism is used to filter the resulting translations. The real monolingual data is combined with the filtered translations to create a pseudo-parallel corpus, which is then used alongside the real parallel corpus for further forward machine  
Figure 4: Flowchart for parallel corpus data enhancement based on pivot language transformation. translation model training. The trained translation system is then utilized repeatedly to generate new pseudo-parallel data to replace the previous data, and the process is shown in Figure 5.  
Nevertheless, in the case where there is only one reference translation, it is challenging to use a similarity measure evaluation method that fulfils the requirements of the application. In order to better evaluate the machine translation method, a text similarity measure algorithm based on the ordering of common words was proposed. This algorithm has less computational overhead and can characterize more cases than the similarity measure based on the editing distance. The process of similarity computation can be described as follows:  
Firstly, do the partitioning of \(\mathit{Sentence_{1}}\) and \(\mathit{Sentence_{1}}\), assuming that they are divided into \(k\) words and l words, respectively, and store the common words of the two clauses in the order of the original sentence in the arrays \(A\) and \(B\), respectively. Sorting in terms of \(A\) to \(B\). Assuming the length of the array is \(n\) and letting \(m=0\), the sorting can be described as:  
step 1: _for i = 0 to \(n-1\)_  
step 2: _for j = 0 to \(n-i-1\)_  
step 3: If the order of \(A[j]\) and \(A[j+1]\) is different from the order of the corresponding contents within \(B\), then perform a bubble sort. step 4: \(m=m+1\)  
It can be seen that \(m\) is the number of executions of the bubbling sort. The similarity of arrays \(A\) and \(B\) can be described as:  
\[Array_{similarity(A,\,B)}=1-\frac{2m}{n\cdot(n-1)} \tag{6}\]  
The similarity calculated from Equation (6) can be extended to the Position Based Sentence Similarity (PBSS) approach. PBSS based on the positional ordering of shared words can be quantitatively represented as:  
\[P(Y)=P(T|S)=\prod_{i=1}^{N}p(T_{i}|S;\theta) \tag{2}\]0 Flowchart of corpus filtering strategy under low resources. \[P(Y)=P(T|S)=\prod_{i=1}^{N}p(T_{i}|S;\theta) \tag{2}\]1  
If there are words that appear differently in \(Sentence_{1}\) and \(Sentence_{2}\), the computation process in Equation (7) may have multiple groups \((A,B)\), and the similarity of the two sentences is taken as the maximum value obtained from the calculation of the different groups \(\big{(}A_{x},B_{y}\big{)}\).