## 2 Related Works
### English Task Oriented Dialogue Systems  
Given the availability of multi-domain English task-oriented dialogue datasets, work on task-oriented DS in the language has progressed from modularized modeling to generative and end-to-end modeling. Given the fact that the traditional DS design complicates tracking the module responsible for interaction failure [6], some studies have built DS using the end-to-end paradigm [7; 8; 9; 10; 11; 12; 13; 14; 15]. However, building powerful task-oriented DS still engenders many challenges due to the system design complexity and the limited availability of human-annotated data. Therefore, the research community has focused on working with the pre-trained language models to reduce human supervision to the extent possible. This approach involves fine-tuning these models and helping to transfer the prior knowledge to improve various NLP tasks, including task-oriented DS. Large pre-trained language models, such as GPT2 and T5, have been used for various NLP tasks, especially language generation tasks. These new approaches model the dialogue pipeline in an end-to-end manner [6].  
Given the high costs associated with data collection and annotation, researchers tend to train their models with the least number of samples using transfer learning. Transfer learning represents one of the most successful few-shot learning approaches for task-oriented DS. It refers to pre-training large language models on text or task-related data and then fine-tuning on a few samples. Such systems have proved their success in task-oriented DS such as the work presented in [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22].  
The task-oriented DS literature includes two study categories: studies targeting only DST and studies targeting both DST and response generation. Dialogue state tracking mainly helps to extract the goals (intents) and slot-value pairs from the conversation to maintain the dialogue belief state (BS) and the summary of the dialogue history. The BS contains information about the dialogue from the system perspective [6]. At each user turn during the conversation, the input to the DST comprises the previous BS, the outputs of the intent classification (the goal), and slot filling information; thus, the DST output is the new/updated BS. For end-to-end dialogue generation, the system indicates the correct required information and generates the appropriate response.  
For the first category, studies targeting only DST, some studies focus on handling the DST task to guarantee building a good base for the whole dialogue system [23, 24, 25, 26, 27, 28, 29]. Meanwhile, other studies have targeted both DST and response generation in an end-to-end manner [11, 12].  
Table 1 summarizes the available models for task-oriented DS in English, including datasets and performance measures. Although the models have achieved promising results, they have been designed for English-language task-oriented DS, and, to the best of our knowledge, no research exists concerning Arabic-language task-oriented DS.  
Nonetheless, the promising performance of pre-trained language models for English-language task-oriented DS has prompted efforts to produce multi-lingual models for task-oriented DS in other languages. Many of these languages are considered low-resource languages due to the absence of high-quality data in the language, and most existing task-oriented DS do not support low-resource languages, creating a gap between the performance of low-resource language systems and high-resource systems. Therefore, providing datasets for low-resource languages is critical to driving the development of efficient end-to-end task-oriented DS for these languages. Several existing studies have built task-oriented DS for low-resource languages using cross-lingual transfer learning [1, 30, 31]. This involves transferring knowledge from high-resource to low-resource languages, enabling the satisfactory performance of end-to-end task-oriented DS.