## 4 Experiments
### Experiments
In the experiments described in [1], the models were trained on almost the same number of English and Chinese dialogues (2952 and 2835). However, our Arabic-TOD dataset includes only 27% of the data included in the BiToD datasets. Accordingly, we investigated two cases:  
* Non-equivalent (NQ): The size of the Arabic-TOD dataset is not equal to the English BiToD dataset. We trained the model with 1000 Arabic dialogues and 2952 English dialogues. * Equivalent (Q): The size of the Arabic-TOD dataset and the English BiToD dataset are equal (1000 dialogues for training). Because three languages were used to train and test the model for the multi-lingual experiment, the mT5 model was trained on the Arabic-TOD, the English BiToD, and the Chinese BiToD datasets [1]. As in the previous experiment, we investigated two cases:  
* Non-equivalent (NQ): The size of the Arabic-TOD dataset is not equal to the English or Chinese BiToD dataset. We trained the model with 1000 Arabic dialogues, 2952 English dialogues, and 2835 Chinese dialogues. * Equivalent (Q): The size of the Arabic-TOD dataset, the English BiToD dataset, and the Chinese BiToD are equal (1000 dialogues for training). For the bi-lingual setting, Table 5 compares the AraConv results--- in terms of BLEU, APIACC, TSR, DSR, and JGA---to the experiments reported in [1] regarding English and Chinese dialogues with the same settings. We observed that the non-equipollent bi-lingual AraConv model (AraConvBi-NQ) outperformed the equipollent bi-lingual AraConv model (AraConvBi-Q), demonstrating the impact of training dialogue dataset size on the final model given that the AraConvBi-NQ model is trained on more data. Therefore, using more English data in training with Arabic helps to improve the result because of the semantics of the conversation, which is almost similar to Arabic, especially for the task-related words. However the model in [1], which was trained on both English and Chinese data and then tested on English, outperformed all models, assuming the dialogues in the two datasets were almost the same. As discussed, the distinguished performance of the English model could have been due to the amount of English data used to train the mT5 model. Nonetheless, we observed that the AraConv model performed better according to the BLEU metric than the Chinese model, despite training on the same English dataset (as a second dataset for joint-training), confirming the greater fluency of the AraConv model. For the multi-lingual setting, Table 6 presents AraConv results calculated in terms of BLEU, APIACC, TSR, DSR, and JGA. Our findings emphasize the previous results of AraConv in the bi-lingual experiment, which saw the non-equipollent multi-lingual AraConv model (AraConvM-NQ) perform better than the equipollent multi-lingual AraConv model (AraConvM-Q). Accordingly, we recognize that joint-training on multiple languages including the target language (in this case, Arabic) improves the results in experiments on the target language, which aligns with the results reported in [30]. Generally, for bi-lingual and multi-lingual experiments, the trained models can simultaneously handle dialogues in multiple languages (whether English, Chinese, or Arabic) without using any of the language identifiers supplied during testing. For the human review, we aimed to rate dialogue or utterances on the basis of certain metrics identified in the literature [56]. Five expert researchers (who are independent from this paper author) were chosen for this task. We randomly selected 20 complete dialogue sessions from the generated dialogues of AraConv model. The researchers were asked to rate these dialogues by providing language understanding and response appropriateness scores. Their scores ranged from 0 (extremely bad) to 5 (extremely good), depending on the system's response. Subsequently, we evaluated the reliability of their rating using Fleiss' Kappa [64].