## 5 Discussion  
According to the results, both the XLM-Roberta and mBART models perform better on some language pairings than others based on accuracy and F1 scores. Figures 3 and 4 illustrate the impact of translation on model accuracy across different language pairs. The data reveal that accuracy significantly decreases when translating between languages with dissimilar structures, such as Urdu to French or Thai to Hindi. Conversely, the reduction in accuracy is less pronounced when translating between languages with similar linguistic patterns, such as English to German or Arabic to English. Despite these variations, the overall trend shows that translation introduces superficial patterns that generally lead to a decline in accuracy in almost all cases after translation. Based on the Urdu-Hindi language pair, the accuracy scores of the XLM-Roberta model are 0.65220 and 0.62188, respectively. In the Urdu-French language pair, accuracy ratings are lower, with original and translated F1 scores of 0.65220 and 0.59863, respectively. mBART performs well with English-French and English-Spanish language pairs, with excellent accuracy scores for both the original and translated F1. Other language combinations, such as Turkish-Urdu and Hindi-Urdu, score lower. The asymmetric effects of fine-tuning on different language pairs have also been observed in recent work. [PERSON] et al. [15] evaluated the performance of the mBART50 multilingual model on a Farsi dataset of dislocations. They found that fine-tuning mBART50 using French-Farsi aligned data dramatically improved the grammatical well-formedness of the French translations, even though some semantic issues remained. However, when they replicated the experiment with Farsi-English fine-tuning, the translations to English were sometimes worse than the baseline mBART50 model. This suggests that the success of fine-tuning multilingual models can be heavily dependent on the specific language pair and the quality of the fine-tuning data available. While adding even a small amount of aligned data in one language direction may be sufficient to improve morpho-syntactic performance, the same may not hold true for the other translation direction. Maintaining a balance between improving grammatical well-formedness and preserving semantic coherence appears to be a key challenge when fine-tuning these models for cross-lingual tasks. Due to the dearth of information in languages other than English, multilingual contextualized word embeddings have been created to simulate universal language representations. However, the translation process itself might result in the creation of novel, surface-level patterns that have an impact on model performance. The translation process itself might result in the creation of novel, surface-level patterns that have an impact on model performance. Recent research by [PERSON] and [PERSON] [16] provides evidence that translation can act as an adversary for NLP models by introducing perturbations that simulate the effect of code-mixing in multilingual communities. The authors demonstrate the effectiveness of their translation-based attack methods, BUMBLEBEE and POLYGLOSS, on state-of-the-art cross-lingual NLI and QA models, proving the effectiveness of translation in creating adversaries. It is possible that accuracy decreases after translation in some language pairs for several reasons. Some of these reasons include:  
The differences in the grammatical, syntactic, and vocabulary structures of different languages can impact the accuracy of translations. Certain language pairs may have more similarities in these linguistic features than others. This similarity can facilitate the transfer of knowledge between languages, leading to improved accuracy and F1 scores. However, for language pairs that are significantly dissimilar, the model may face greater challenges and require more training time and data to achieve optimal performance.