## 2 Background
### Quality Assessment Indicators
The evaluation of each translation is determined by the following:  
\[F_{\beta}=\left(1+\beta^{2}\right)\cdot\frac{P\cdot R}{\beta^{2}\cdot P+R}\]  
where \(P\) represents precision, \(R\) denotes recall, and \(\beta\) serves as the weighting parameter. The scores produced by this metric always range between 0 and 1, with higher values indicating superior translation quality. METEOR [46] is a string-based metric developed to address the limitations of traditional metrics such as BLEU, which primarily focus on exact word matches, n-gram matches, or overall sentence length. METEOR incorporates multiple factors to generate a more refined evaluation score, surpassing the effectiveness of simple word-matching techniques. To achieve this, in addition to exact word matches, it considers synonym matches, morphological variations (e.g., \"stop\" and \"stopping\"), and word order alignments. Like previous metrics, METEOR relies on a reference translation as input. Before conducting its calculations, it determines the value of \(m\), which represents the number of words in the hypothesis translation that also appear in the reference translation. The translation score is computed using the following expression:  
\[\text{METEOR}=F_{\text{mean}}\cdot\left(1-\text{Penalty}\right)=\frac{P\cdot R }{\alpha\cdot P+\left(1-\alpha\right)\cdot R}\cdot\left(1-\gamma\cdot\text{ frag\_frac}{}\delta\right)\]  
where \(\gamma\) is the weight assigned to the penalty, \(\beta\) controls the shape of the penalty function, and  
\[P=\frac{m}{\left|\text{hypothesis}\right|}\]  
where \(\left|\text{hypothesis}\right|\) is the length of the hypothesis translation,  
\[R=\frac{m}{\left|\text{reference}\right|}\]  
where \(\left|\text{reference}\right|\) is the length of the reference translation, and  
\[\text{frag\_frac}=\frac{ch}{m}\]  
where \(ch\) represents the number of fragments. The scores generated by METEOR range from 0 to 1, with higher values indicating superior translation quality. RIBS (Rank-based Intuitive Bilingual Evaluation Score) [47] is a lexical metric specifically designed for languages with significant syntactic differences. It primarily evaluates the order and correlation of words between the reference and hypothesis translations, employing a rank-based positional correspondence approach to achieve this analysis. To assess word order, RIBES computes [PERSON]'s \(\tau\) coefficient. Additionally, it incorporates a length penalty in the final score to prevent truncated sentences from receiving disproportionately high scores. The formula for calculating RIBES is given by the following:  
\[\text{RIBES}=\text{nkt}\times(\text{p1}^{\alpha})\times(\text{bp}^{\beta}) \tag{4}\]  
where  
* nkt: [PERSON]'s \(\tau\) coefficient;
* p1: unigram precision;
* bp: length penalty;
* \(\alpha\), \(\beta\): hyperparameters that control the weight of unigram precision and length penalty, respectively. TER (Translation Edit Rate) [48] is a string-based metric designed to quantify the human effort required to transform a machine-generated translation into a perfect human reference. It computes the proportion of minimal edits necessary, including insertions, deletions, substitutions, and reordering operations. Given this approach, a higher TER score signifies a greater effort required to refine the hypothesis translation. The final score is calculated as follows:  
\[\text{TER}=\frac{\text{\#substitutions}+\text{\#insertions}+\text{\#deletions}+ \text{\#shifts}}{\text{length (reference)}} \tag{5}\]  
The TER score ranges from 0 to 1, where a lower score (closer to 0) indicates a higher quality translation, as it requires fewer modifications. This aspect is particularly relevant when comparing TER values with those generated by other evaluation metrics. YiSi (YiSi--Semantic Similarity Evaluation) [49] employs semantic embeddings, such as those derived from pretrained language models, to capture deeper relationships between words beyond surface-level lexical matches.