## 4 Experiments and Discussion
### Experiment Results and Analysis  
The proposed model is compared with several baseline and multilingual models. Accuracy and F1-scores are adopted as the evaluation metrics to ensure a consistent and fair comparison. The experimental results of classification accuracy and F1-score on the Amazon Review dataset are shown in Table 2 and Table 3, and the results of classification accuracy and F1-score on the XGLUE and SLU datasets are shown in Table 4 and Table 5, respectively. Table 2 presents the accuracy results for the Amazon Review dataset; the proposed model achieves excellent performance across the three language datasets, outperforming not only the baselines but also state-of-the-art models in all domains and languages. Notably, the highest accuracy of 93.22% is reached on the German book review dataset. Furthermore, all models exhibit higher accuracy on the French and German datasets compared to Japanese, suggesting that linguistic similarity contributes to improved model performance. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**German**} & \multicolumn{4}{c}{**French**} & \multicolumn{4}{c}{**Japanese**} \\ \cline{2-10}
**Model** & **Books** & **DVD** & **Music** & **Books** & **DVD** & **Music** & **Books** & **DVD** & **Music** \\ \hline mBERT & 84.35 & 82.85 & 83.85 & 84.55 & 85.85 & 83.65 & 73.35 & 74.80 & 76.10 \\ XLM & 86.85 & 84.20 & 85.90 & 88.10 & 86.95 & 86.20 & 80.95 & 79.20 & 78.02 \\ XLM-R & 91.65 & 87.60 & 90.97 & 89.33 & 90.07 & 89.15 & 85.26 & 86.77 & 86.95 \\ mT5 & 91.83 & 88.82 & 91.98 & 91.02 & 91.42 & 90.36 & 87.41 & 88.25 & 87.82 \\ CLHG & 92.70 & 88.60 & 91.62 & 90.67 & 91.38 & 90.45 & 87.21 & 87.33 & 88.08 \\ TGCTC & 92.63 & 87.91 & 92.54 & 90.03 & 91.35 & 88.68 & 86.50 & 88.27 & 89.07 \\ Meta-X\({}_{NLG}\) & 90.34 & 88.37 & 89.64 & 90.33 & 90.38 & 90.26 & 87.25 & 88.91 & 87.79 \\ EM & 92.73 & 90.06 & 91.83 & 91.55 & 92.03 & 91.47 & 88.90 & 89.06 & 89.14 \\ LS-mBERT & 92.43 & 90.57 & 91.85 & 92.30 & 90.57 & 92.52 & 87.62 & 89.41 & 88.37 \\ XCLHG & **93.22** & **91.31** & **92.91** & **92.98** & **92.64** & **93.13** & **89.16** & **90.31** & **89.73** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%) on Amazon Review dataset. Table 4 presents the results on two datasets, including the tasks of news and intent classification. The model XCLHG consistently outperforms all other models listed in the table. Notably, the model achieves an accuracy of up to 97.67% on the SLU Spanish dataset. This excellent performance can be attributed to two key factors: (1) the linguistic similarity of the languages involved, as discussed earlier, and (2) the shorter text lengths in the SLU dataset, which facilitate the construction of a more stable GAT. Additionally, both CLHG [18] and TG-CTC [28] employ graph-based approaches and achieve significantly higher performance compared to models evaluated on other datasets. The most significant improvement, compared to the suboptimal model, is observed on the XGLUE dataset, where a 2.20% increase in performance is achieved for Russian. As Russian is the most dissimilar language to English in the XGLUE dataset, this demonstrates that the proposed contrastive learning strategy effectively reduces the linguistic discrepancy between languages.