## 5 Conclusions and Future Work  
To the best of our knowledge, this work represents to the first attempt to build an end-to-end Arabic task-oriented dialogue system (AraConv) using a pre-trained transformer-based multi-lingual language model. We utilized the highly regarded multi-lingual model mT5 to build an end-to-end Arabic task-oriented dialogue system with different settings and presented an Arabic-TOD dataset based on translating 27% of the BiToD dataset's English dialogue data into Arabic. The Arabic-TOD dataset is considered the first dialogue dataset for the Arabic task-oriented DS that supports code-switching. Although using the Arabic-TOD dataset to train and test the model in a mono-lingual setting demonstrates a reasonable performance for the AraConv model compared to the results observed for the English and Chinese BiToD datasets in the same settings, the performance is undermined by the small size of the Arabic TOD dataset. To overcome this problem, we considered joint-training the model on Arabic dialogue data and one or two high-resource languages (English or both English and Chinese). The findings reveal that the AraConv model in the multi-lingual setting outperformed the AraConv model in the mono-lingual setting, with multi-lingual training with English, Chinese, and Arabic observed to be better than bi-lingual training with only English and Arabic data. Thus, the AraConv model can be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TSR** & **DSR** & **APIAcc** & **BLEU** & **JGA** \\ \hline AraConvM-NQ (**AR**, EN, ZH) & **51.27** & **20.00** & **55.44** & **32.58** & **37.68** \\ \hline AraConvM-Q (**AR**, EN, ZH) & 47.17 & 16.98 & 53.07 & 31.05 & 36.13 \\ \hline \end{tabular}
\end{table} 
Table 6: Multi-lingual experiment DST and end-to-end dialogue generation results for the AraConv model on the Arabic-TOD dataset. The bold letters refer to the target language. Bold numbers indicating the best result according to the column’s metric value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TSR** & **DSR** & **APIAcc** & **BLEU** & **JGA** \\ \hline \hline  & \multicolumn{3}{c}{**Arabic**} & & \\ \hline AraConvBi-NQ (**AR**, EN) & 45.57 & 21.90 & 56.23 & 30.41 & 37.35 \\ \hline AraConvBi-Q (**AR**, EN) & 44.62 & 16.98 & 46.32 & 27.36 & 35.58 \\ \hline \hline  & \multicolumn{3}{c}{**Other languages**} & & \\ \hline ZH, **EN**[1] & **71.18** & **51.13** & **71.87** & **40.71** & **72.16** \\ \hline
**ZH**, EN [1] & 57.24 & 34.78 & 65.54 & 22.45 & 68.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bi-lingual experiment DST and end-to-end dialogue generation results for the AraConv model trained on the Arabic-TOD dataset compared to the bi-lingual BiToD experiments [1] using English and Chinese BiToD datasets. The bold letters refer to the target language in the corresponding experiments (used to test the model). Bold numbers indicating the best result according to the column’s metric value. considered a good baseline for building robust end-to-end Arabic task-oriented DS that can engage with complex scenarios. The main limitation of this work is the small size of the Arabic-TOD dataset. A related limitation concerns the Arabic-TOD dataset using non-Arabic entities, with the dataset code-switching due to entities in the original BiToD dataset. However, we leveraged this property to align the model with the routine usage of such entities in conversation. In the future, we aim to extend the Arabic-TOD dataset to equal the BiToD dataset in terms of the number of dialogues. Additionally, we plan to examine cross-lingual models, especially involving the Arabic-TOD dataset. Furthermore, we plan to develop Arabic task-oriented DS using other multilingual language models (e.g., mBART [65]). Another possible venue for future work is using a pre-trained Arabic model for Arabic task-oriented DS such as AraT5 [66], which was yet to be deployed at the time of working on this paper. Conceptualization, A.F. and M.A.-Y. ; methodology, A.F. ; software, A.F. ; validation, A.F.