# A Kazakh-Chinese Cross-Lingual Joint Modeling Method for Question Understanding
## 4 Experiments  
### Datasets  
The SNIPS [27] dataset is derived from the Snips personal voice assistant, containing 13,784 training samples and 700 test samples. It covers 7 different intent categories and 39 entity categories, with content related to various topics in daily life. The ATIS [28] dataset includes 4478 training samples and 1393 test samples, covering 21 intent categories and 60 entity categories. The data primarily involves user queries and dialogues commonly found in the context of air travel. The Multilingual Task-Oriented Dialogue (MTOD) dataset [29] used in this paper contains dialogue samples from three languages (English, Spanish, and Thai), offering a broader coverage. The dataset includes 30,521 English training samples, 3617 Spanish training samples, and 2156 Thai training samples, covering 12 intent categories and 11 entity categories across multiple domains and tasks, such as restaurant reservations, hotel inquiries, movie recommendations, etc. MTOD is designed for multi-lingual task-oriented dialogues and enables knowledge sharing and performance enhancement in a multi-lingual environment through cross-lingual pre-training models and dialogue state transfer. It holds significant practical value in the development and application of multi-lingual question-answering systems. MTOD uses English as the source language, and experiments are conducted according to the original dataset division. The training samples consist of 2879 sentences and 22,104 characters, the validation samples include 1977 sentences and 13,571 characters, and the test samples contain 2215 sentences and 15,944 characters. The target languages are Spanish and Thai, with 2600 sentences and 9132 characters used for zero-shot training method evaluations. The detailed information of the public datasets is shown in Figure 3.  
Most existing public question understanding datasets primarily support high-resource monolingual corpora, such as Chinese, English, etc., while research involving low-resource language question understanding datasets is relatively limited. Only the MTOD dataset supports multi-lingual question understanding tasks in English, Spanish, and Thai. Due to the limited research on low-resource question understanding, there is currently no publicly available multi-lingual question understanding corpus that includes minority languages in China. To address the scarcity of minority language corpora, this chapter constructs a cross-lingual tourism question dataset (Cross-lingual Tourism Field Question Dataset,  
Figure 3: Detailed statistical information of public question understanding datasets. JISD) that includes Chinese and Kazakh. The original tourism information data are sourced from tourism websites like Baidu Encyclopedia, Qunar, and Ctrip, covering famous tourist attractions and related information from 34 provinces, municipalities, autonomous regions, and special administrative regions across China. The sample display of the original tourism data are shown in Figure 4. The original Kazakh dataset shown in this figure is based on the Arabic alphabet and is briefly described in Appendix A. It is worth noting that this writing system differs from the Cyrillic (1940-2021) and Latin (introduced since 2021) systems used in Kazakhstan, but is interoperable through phonemic transcription. After organizing and analyzing the original tourism information data and considering the relevance and accuracy between intent and slot, a total of 27 intent categories were designed, including transportation, food, location, tickets, etc., as well as 13 slot categories, including attraction names, types, climate, and levels. The statistics of intent and slot categories are shown in Figure 5.  
Based on the designed intent categories and entity categories, question word templates were constructed, using attraction names and provincial/city names from the original tourism information data as the main body. These generated Chinese tourism question data cover all intent and entity categories nationwide.