# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.2 Ablation Experiments  
The comparative experiment analyzed the cross-lingual transfer performance of the Bi-XTM model on the joint task of question understanding. To further investigate the positive impact of the subtask interaction module on the model, the following ablation experiments were conducted: (1) Encoder: The probability outputs generated by the cross-lingual shared encoder are directly fed into the intent and slot decoders without adding any explicit interaction layers. (2) w/o BP: The subtask interaction layer is removed to explore the effect of collaborative interaction attention on the model's performance. (3) w/o CA: The collaborative interaction attention layer is removed, keeping the subtask interaction layer, and the word-level encoding method is used to understand the positive impact brought by the subtask interaction layer. (4) Intent: Information transmission from intent vectors to slot vectors in the interaction layer is canceled. (5) Slot: Information transmission from slot vectors to intent vectors in the interaction layer is canceled. Tables 8 and 9 show the ablation experiment results of the Bi-XTM model on the target language data of the MTOD and JISD datasets, respectively. After removing the subtask interaction module, the model degrades into an implicit joint model (Encoder), exhibiting the worst cross-lingual transfer performance across the three target language corpora. This is because relying solely on shared low-level representations does not enable the model to learn the direct correlation between intent vectors and slot vectors. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Spanish**} & \multicolumn{3}{c}{**Thai**} \\ \cline{2-7}  & **Acc** & **F1** & **Sent.Acc** & **Acc** & **F1** & **Sent.Acc** \\ \hline Encoder & 88.2 & 71.2 & 66.7 & 52.2 & 44.8 & 34.5 \\ w/o BP & 90.2 & 70.1 & 68.1 & 56.9 & 44.2 & 35.5 \\ w/o CA & 89.6 & 73.8 & 61.4 & 55.3 & 44.8 & 36.8 \\ Intent & 90.2 & 73.7 & 68.4 & 56.3 & 44.1 & 36.5 \\ Slot & 91.6 & 75.3 & 68.3 & 57.0 & 44.7 & 37.0 \\ Bi-XTM & 92.2 & 77.3 & 68.6 & 57.8 & 44.9 & 37.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MTOD ablation experiment results. When the subtask interaction layer is removed (w/o BP), the model shares local information of intermediate-layer intent and slot vectors through the collaborative interaction attention mechanism. Under this setting, intent accuracy shows a slight improvement, but the slot \(F1\) score drops significantly. This indicates that the attention layer captures key local information from the characters, allowing the model to predict intents while also capturing the slot information associated with those intents. Such contextual association helps improve the accurate understanding of user input and better semantic modeling. However, when the model uses sentence-level encoding for transmission, if an incorrect intent label is recognized, the slot vector will receive noisy information from the intent, leading to a misunderstanding of slot information and thus causing errors in slot recognition. When the collaborative interaction attention layer is removed (w/o CA), the improvement in intent accuracy is not significant, but the error propagation caused by sentence-level encoding is effectively mitigated. In the one-way transmission methods (Intent and Slot), cutting off either the intent-to-slot or slot-to-intent transmission results in only slight performance differences, and the receiving side of the one-way propagation generally achieves better performance. This is due to the error correction phenomenon induced by cutting the interaction loop in one-way propagation. Although this helps reduce error propagation, it also blocks the positive transmission of intent or slot information.