# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.1 Comparative Experiments
Compared to the baseline models, the Bi-XTM model achieved significant improvements in intent classification accuracy across all languages (English, Spanish, and Thai). Furthermore, on English and Spanish corpora, the Bi-XTM model effectively alleviated the error propagation issues caused by bidirectional information transfer, resulting in notable improvements in slot \(F1\) scores. However, the model's slot filling performance on Thai data did not reach optimal levels, as the shared encoder introduced minority language semantic vector representations, and shared information between different language families may have been interfered with, leading to data bias. Future research could address this issue by expanding the training samples and labels, including more low-resource languages, such as Thai, to improve the model's generalization capability in low-resource scenarios. For English corpora, although the Bi-XTM model showed a slight decrease in sentence-level accuracy, its advantage lies in more precisely controlling the bidirectional relationships and information transfer between subtasks in cross-lingual scenarios, resulting in overall good generalization. Therefore, the slight accuracy drop on certain languages is a trade-off to achieve better model generalization. The comparative experimental results of the Bi-XTM model on the MTOD dataset demonstrate that introducing the subtask interaction module in cross-lingual pre-training models helps alleviate the error propagation issues caused by bidirectional information transfer, positively impacting the joint tasks of intent recognition and slot filling. However, the Bi-XTM model shows performance gaps of 17.8%, 36.6%, and 37.8% in Acc, F1, and Sent.Acc scores between Spanish and Thai, respectively, indicating that cross-lingual transfer between languages from different language families remains challenging. Specifically, the model's recognition accuracy on Thai is low. This is primarily because Spanish and English, as widely spoken global languages, have abundant publicly available data resources, allowing the model to fully learn their grammatical and semantic features from large-scale corpora and thus form more accurate representations. In contrast, Thai and minority languages suffer from a lack of data, making it difficult for the model to capture their unique linguistic patterns, which limits classification performance. Spanish and English both belong to the Indo-European language family and share high similarity in grammatical structures and vocabulary composition, enabling the model to transfer and share linguistic knowledge through transfer learning. However, Thai belongs to the Kra-Dai language family, and minority languages such as Tibetan and Mongolian have unique writing systems and grammatical rules that differ significantly from mainstream languages. As a result, it is difficult for the model to directly reuse existing knowledge. These languages require additional adaptation to their specific linguistic characteristics, but due to limited data availability, the model is unable to effectively learn these features, which negatively affects classification performance. Tables 5-7 show the comparative experimental results of the Bi-XTM model on the JISD dataset. During the pre-training phase, both the mBERT and XLM-R models used a large amount of multi-lingual data; however, these datasets lacked sufficient Kazakh data, preventing the models from fully learning the linguistic features and semantic information of Kazakh, leading to poor performance in cross-lingual scenarios. The CINO model uses a large amount of data from Chinese minority languages, including a significant amount of Kazakh data, enabling the CINO model to better understand and learn Kazakh's grammatical structures and semantic rules. The rich data volume and diversity provide the model with a more comprehensive linguistic background and knowledge, significantly improving its recognition performance in Kazakh.