## 2 Related Works
### Arabic Task-Oriented Dialogue Systems  
Considering the maturity of research concerning English-based task-oriented DS, we find that task-oriented DS research more broadly remains in its infancy for Arabic. This is due to a lack of fundamental NLP resources and a scarcity of datasets for Arabic task-oriented DS. Most of the research on Arabic task-oriented DS focuses on achieving specific tasks, such as intent classification [34, 35, 36] and entity classification [34]. However, there \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Back-Bone Models**} & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{3-8}  & & & **BLEU** & **Inform Rate** & **Success Rate** & **JGA** \\ \hline DAMD [32] & MultiWOZ 2.1 & multi-decoder seq2 seq & 16.6 & 76.4 & 60.4 & 51.45 \\ \hline Ham [10] & MultiWOZ 2.1 & GPT-2 & 6.01 & 77.00 & 69.20 & 44.03 \\ \hline SimpleToD [11] & MultiWOZ 2.1 & GPT-2 & 15.23 & 85.00 & 70.05 & **56.45** \\ \hline SC-GPT [16] & MultiWOZ & GPT-2 & **30.76** & - & - & - \\ \hline SOLOIST [12] & MultiWOZ 2.0 & GPT-2 & 16.54 & 85.50 & 72.90 & - \\ \hline MARCO [33] & MultiWOZ 2.0 & - & 20.02 & 92.30 & 78.60 & - \\ \hline UBAR [13] & MultiWOZ 2.1 & GPT-2 & 17.0 & **95.4** & **80.7** & 56.20 \\ \hline ToD-BERT [17] & MultiWOZ 2.1 & BERT & - & - & - & 48.00 \\ \hline \multirow{3}{*}{MinTL [14]} & \multirow{3}{*}{MultiWOZ 2.0} & T5-small & 19.11 & 80.04 & 72.71 & 51.24 \\  & & T5-base & 18.59 & 82.15 & 74.44 & 52.07 \\  & & BART-large & 17.89 & 84.88 & 74.91 & 52.10 \\ \hline LABES-S2S [20] & MultiWOZ 2.1 & A copy-augmented Seq2 Seq & 18.3 & 78.1 & 67.1 & 51.45 \\ \hline AuGPT [21] & MultiWOZ 2.1 & GPT-2 & 17.2 & 91.4 & 72.9 & - \\ \hline GPT-CAN [15] & MultiWOZ 2.0 & GPT-2 & 17.02 & 93.70 & 76.70 & 55.57 \\ \hline HyKnow [22] & MultiWOZ 2.1 & multi-stage Seq2 Seq & 18.0 & 82.3 & 69.4 & 49.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing the performances of the most common English-based task-oriented dialogue systems (DS). Bold numbers indicating the best system according to the column’s metric value. some attempts to build task-oriented DS have investigated specific domains, including home automation [34], flight bookings [37], education [38; 39; 40], hotel reservations [41], and Islamic knowledge enquires [42]. Some Arabic task-oriented DS have been designed to specifically serve the Arabic dialects (e.g., OlloBot [43] and Nabiha [44]). However, this review excludes some of these studies because they are categorized as chatbots rather than task-oriented DS because their system design does not follow a task-oriented DS structure [39; 40; 42; 43; 44]. Notably, [PERSON] et al. [34] used deep learning approaches to build a natural language understanding module for Arabic task-oriented DS for home automation. The module manages of both intent classification and entity extraction tasks. For intent classification, it uses LSTMs and CNNs; for entity extraction, BiLSTM and character-based word embeddings are used. The study used data collected via an online survey and the AQMAR dataset. The data were filtered and labeled according to the Conll-2003 NER format. The findings for the intent classification demonstrated that CNNs performed better than LSTMs (F-score = 94%). For entity extraction, the model obtained comparable results to the named entity recognition benchmarks in English (F-score = 94%). Meanwhile, [PERSON] et al. [35] used a multi-class hierarchical model to solve the dialogue acts classification issue associated with Arabic dialects. They used a manually collected and annotated dataset from multi-genre Egyptian call centers to evaluate their system performance. Using an SVM classifier produced an average F-score of 91.2%, indicating an improvement of 20% compared to the state-of-art approach. Elsewhere, [PERSON] et al. [36] examined different machine learning approaches to recognizing user acts in a text-based DS for the Levantine Arabic dialect.