## 4 Experiments and Discussion
### Experiment Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**XGLUE**} & \multicolumn{2}{c}{**SLU**} \\ \cline{2-7}
**Model** & **German** & **French** & **Spanish** & **Russian** & **Spanish** & **Thai** \\ \hline mBERT & 82.53 & 78.46 & 81.83 & 79.06 & 74.91 & 72.97 \\ XLM & 83.52 & 78.83 & 82.49 & 79.47 & 62.30 & 81.60 \\ XLM-R & 84.61 & 78.96 & 83.51 & 79.94 & 94.38 & 85.17 \\ mT-5 & 85.03 & 79.62 & 84.53 & 81.21 & 95.68 & 87.32 \\ CLHG & 85.00 & 79.58 & 84.80 & 79.91 & 96.81 & 89.71 \\ Meta-X\({}_{NLG}\) & 83.41 & 79.04 & 84.77 & 78.02 & 87.12 & 84.33 \\ EM & 84.52 & 79.31 & 85.13 & 79.70 & 85.64 & 79.29 \\ LS-mBERT & 85.78 & 80.24 & 85.61 & 80.14 & 82.07 & 63.43 \\ TGCTC & 85.36 & 80.45 & 85.65 & 80.32 & 97.23 & 90.85 \\ XCLHG & **86.43** & **82.43** & **87.31** & **82.52** & **97.67** & **92.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy (%) on SLU and XGLUE datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**German**} & \multicolumn{2}{c}{**French**} & \multicolumn{2}{c}{**Japanese**} \\ \cline{2-10}
**Model** & **Books** & **DVD** & **Music** & **Books** & **DVD** & **Music** & **Books** & **DVD** & **Music** \\ \hline mBERT & 82.71 & 81.26 & 82.11 & 82.97 & 84.35 & 82.10 & 71.83 & 73.26 & 74.39 \\ XLM & 85.14 & 82.56 & 84.48 & 86.62 & 85.41 & 84.73 & 79.24 & 77.20 & 76.52 \\ XLM-R & 90.33 & 86.12 & 89.38 & 86.36 & 87.91 & 86.28 & 83.13 & 84.77 & 85.34 \\ mT5 & 90.72 & 86.90 & 89.98 & 89.14 & 89.74 & 88.02 & 85.71 & 86.53 & 85.90 \\ CLHG & 90.70 & 87.15 & 90.04 & 88.72 & 89.83 & 88.17 & 85.86 & 86.11 & 86.36 \\ Meta-X\({}_{NLG}\) & 88.59 & 86.75 & 80.49 & 88.23 & 88.12 & 88.67 & 85.42 & 86.73 & 85.80 \\ EM & 90.03 & 88.14 & 89.47 & 89.53 & 90.11 & 89.24 & 85.37 & 86.16 & 86.28 \\ LS-mBERT & 90.17 & 88.95 & 90.17 & 90.58 & 90.36 & 89.74 & 86.22 & 87.13 & 86.97 \\ XCLHG & **91.18** & **89.94** & **91.17** & **91.22** & **90.93** & **91.28** & **88.16** & **87.91** & **88.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1-score (%) for text classification on Amazon Review dataset. Tables 3 and 5 present the F1-score results; the predicted F1-scores closely reflect the trends observed in the accuracy results. For the Amazon Review dataset, XCLHG achieves the highest F1-scores across all language subsets. Similarly, on the SLU and XGLUE datasets, XCLHG consistently outperforms competing models, achieving notably high F1-scores, particularly on the SLU Spanish subset, where it reaches approximately 93.52%. These results indicate that XCLHG not only delivers high accuracy but also maintains a balanced precision-recall performance, thereby reinforcing its effectiveness for CLTC. Overall, the alignment between the accuracy and F1-scores further substantiates the robustness of the approach in capturing discriminative features across diverse multilingual contexts and clearly indicates that the XCLHG model significantly outperforms all competing multilingual models. The success of the model can be attributed to two key factors: (1) The design of a heterogeneous graph enables the effective integration of structural features, enhancing the model's ability to capture complex relationships within the data. (2) The incorporation of multi-view contrastive learning facilitates the alignment of semantic representations by bringing together documents with the same label and their translated pairs, thereby enhancing cross-lingual consistency.