# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.1 Comparative Experiments
The Co-Transformer model still faces the issue of error propagation caused by sentence-level intent information transfer. Compared to CINO, this model shows a slight improvement in intent accuracy but reduces slot recognition performance. The Bi-XTM model outperforms the baseline model on all three metrics, demonstrating its excellent cross-lingual transfer ability. However, its overall accuracy has not yet reached a stage where it is fully applicable, so a further comparison experiment using the mixed-language training method is considered. To compare with the mixed-language training method, this experiment also included single-language training, using Chinese and Kazakh corpora as the model's training data to observe the model's performance on different language corpora. Compared to the zero-shot training method, the mBERT and XLM-R models show significant performance improvements on the Kazakh corpus. However, due to the lack of specialized training and optimization for low-resource languages, these two models still exhibit a significant performance gap when compared to the CINO and Co-Transformer models. Although the Bi-XTM model achieves optimal performance on the Kazakh corpus across all three metrics, the model struggles to reach system-level application effectiveness due to data scarcity when trained solely on low-resource single-language corpora. Mixed-language training allows the model to share knowledge between the source and target languages. There may be certain linguistic similarities and shared features between different languages, and mixed-language training enables the model to encounter \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Chinese**} & \multicolumn{3}{c}{**Kazakh**} \\ \cline{2-7}  & **Acc** & \(F1\) & **Sent.Acc** & **Acc** & \(F1\) & **Sent.Acc** \\ \hline \hline \multicolumn{7}{c}{Single-language training} \\ \hline mBERT & 98.3 & 95.0 & 88.9 & 66.5 & 57.1 & 43.8 \\ XLM-R & 96.0 & 98.5 & 95.2 & 67.9 & 57.5 & 51.1 \\ CINO & 99.5 & 97.2 & 97.1 & 90.8 & 71.6 & 65.2 \\ Co-Trans & 99.7 & 96.4 & 97.2 & 91.4 & 69.4 & 65.6 \\ Bi-XTM & 99.4 & 98.9 & 97.6 & 92.1 & 74.6 & 66.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: JISD single-language training comparative experiment results. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Chinese**} & \multicolumn{3}{c}{**Kazakh**} \\ \cline{2-7}  & **Acc** & \(F1\) & **Sent.Acc** & **Acc** & \(F1\) & **Sent.Acc** \\ \hline \hline \multicolumn{7}{c}{Mixed-language training} \\ \hline mBERT & 98.4 & 91.6 & 87.9 & 75.3 & 60.0 & 54.5 \\ XLM-R & 97.6 & 96.0 & 94.8 & 75.6 & 61.7 & 60.5 \\ CINO & 99.2 & 96.3 & 94.2 & 92.1 & 73.0 & 66.2 \\ Co-Trans & 98.7 & 96.9 & 95.1 & 92.8 & 72.1 & 69.7 \\ Bi-XTM & 99.6 & 97.1 & 95.7 & 93.5 & 77.2 & 70.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: JISD mixed-language training comparative experiment results. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Chinese**} & \multicolumn{3}{c}{**Kazakh**} \\ \cline{2-7}  & **Acc** & \(F1\) & **Sent.Acc** & **Acc** & \(F1\) & **Sent.Acc** \\ \hline \hline \multicolumn{7}{c}{zero-shot training} \\ \hline mBERT & 98.3 & 95.0 & 88.9 & 13.3 & 3.4 & 2.4 \\ XLM-R & 96.0 & 98.5 & 95.2 & 19.0 & 5.2 & 3.0 \\ CINO & 99.5 & 97.2 & 97.1 & 42.2 & 30.7 & 11.4 \\ Co-Trans & 99.7 & 96.4 & 97.2 & 44.6 & 28.5 & 11.9 \\ Bi-XTM & 99.4 & 98.9 & 97.6 & 45.2 & 31.8 & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: JISD zero-shot training comparative experiment results. multiple languages during the training phase, allowing it to learn these shared features. From the results of the mixed-language training experiment, it can be seen that the Bi-XTM model achieves optimal performance on the Kazakh corpus across all three metrics. However, the model shows an overall decrease in performance on the Chinese corpus compared to the other two training methods.