# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 5 Conclusions  
This paper addresses the issue that current joint models for question understanding fail to effectively handle the joint task of intent recognition and slot filling in cross-lingual scenarios. It proposes the Cross-lingual Bi-directional Propagation Model (Bi-XTM), which employs a subtask interaction approach to mitigate the error propagation problem caused by sentence-level encoding. At the same time, it fully leverages the output distributions of both intent and slot tasks for bidirectional information transmission, enhancing the interaction between the intent recognition and slot filling subtasks. This approach balances the model's performance on the joint task and improves its generalization ability. This paper also focuses on the use of public question understanding datasets, detailing the methods for data acquisition, data information, and data processing, and it provides some examples from the datasets used. In addition, it introduces the construction process, text annotation, and data processing methods for the Cross-lingual Tourism Field Question \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Kazakh**} \\ \cline{2-4}  & **Acc** & **F1** & **Sent.Acc** \\ \hline Encoder & 42.2 & 30.7 & 11.4 \\ w/o BP & 43.9 & 28.4 & 10.5 \\ w/o CA & 43.3 & 31.6 & 11.6 \\ Intent & 44.1 & 31.2 & 11.3 \\ Slot & 44.3 & 31.6 & 11.5 \\ Bi-XTM & 45.2 & 31.8 & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: JISD ablation experiment results. Dataset (JISD), summarizing the dataset resources utilized in this study. It clearly specifies the names, usage scopes, data volumes, and the number of intents and slots for each dataset. The comparative experimental results of the Bi-XTM model on the MTOD and JISD datasets show that Bi-XTM achieves significant overall performance improvements over the baseline models on both datasets. Through the ablation studies conducted on the target language data of the MTOD and JISD datasets, it can be concluded that incorporating both the co-interactive attention mechanism and the subtask interaction layers into the Bi-XTM model leads to substantial performance gains compared to the implicit joint model, the unidirectional propagation models, and models that use only either the co-interactive attention mechanism or the subtask interaction layers individually. The cross-lingual subtask interaction model addresses the current research gaps in joint question understanding tasks under cross-lingual scenarios. Compared to traditional approaches, this model significantly reduces over-reliance on monolingual data and marks a notable advancement in cross-lingual joint modeling. However, we must also acknowledge that cross-lingual transfer still faces certain limitations in real-world applications, especially for languages like Thai that are relatively low-resource or exhibit substantial linguistic differences. There remains considerable room for performance improvements in such cases. In the future, we plan to further extend the cross-lingual subtask interaction model to cover more low-resource languages. At the same time, we are committed to releasing a larger-scale Kazakh language corpus. A rich and high-quality corpus serves as the cornerstone for model training and optimization. A more extensive Kazakh corpus will provide strong data support for related research, help improve model performance for Kazakh, and offer more valuable samples for cross-lingual studies. Overall, this research presents a novel and explicit joint modeling approach for cross-lingual joint question understanding tasks and offers valuable insights for future advancements in this field. Conceptualization, Y.Y. ; methodology, Y.M. ; software, H.L. ; validation, H.L. ; formal analysis, Y.Y. ; investigation, X.Z. ; resources, Y.M. ; data curation, Y.M. ; writing--original draft preparation, Y.Y. ; writing--review and editing, Y.M. ; visualization, Y.T. ; supervision, Y.Y. ; project administration, Y.M. ; funding acquisition, G.A.