# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 5 Experiments
### RQ3: Can English-Trained Models Effectively Adapt to Divergent Languages?
This finding aligns closely with previous research [51] and suggests a possible connection to the nature of perturbation-based cross-lingual methods, as it operates as a language-independent approach. Our approach focuses on constructing virtual multilingual text embeddings through multistep perturbations, independent of language similarity, thereby enhancing overall cross-lingual capabilities. This suggests that incorporating syntactic perturbations could further improve zero-shot transfer performance. While methods utilizing parallel corpora can enhance transfer capabilities for similar languages, our approach aims to boost performance across a broader range of languages. Despite these advancements, overall performance remains modest due to the inherent challenges of the task. We will continue to refine our method in future work, as it represents a language-independent, cross-lingual approach. In Table 6, we only select some languages with a large amount of data in xMP, because a small amount of data in a language may affect the accuracy of the experimental results, so we only discuss the languages with a relatively sufficient amount of data. In Appendix B, we provide a detailed discussion on the data amount of each language in xMP. Although the data amount of some languages is small, we still retain it in xMP; on the one hand, this will \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Language Family**} & \multicolumn{3}{c}{**Factuality**} & \multicolumn{3}{c}{**Political Bias**} \\  & & **ACC** & **F1-Macro** & **MAE** & **ACC** & **F1-Macro** & **MAE** \\ \hline
**XLM-R** & & & & & & & \\ en & Indo-European (Germanic) & 0.241 & 0.244 & 0.695 & 0.217 & 0.238 & 0.718 \\ ar & Afro-Asiatic (Center-Semitic) & 0.218 & 0.225 & 1.118 & 0.167 & 0.150 & 0.941 \\ de & Indo-European (Germanic) & 0.083 & 0.111 & 1.846 & 0.050 & 0.071 & 0.923 \\ es & Indo-European (Romance) & 0.104 & 0.137 & 1.083 & 0.167 & 0.174 & 0.833 \\ fr & Indo-European (Romance) & 0.103 & 0.136 & 1.276 & 0.146 & 0.173 & 0.724 \\ nl & Indo-European (Germanic) & 0.136 & 0.176 & 1.455 & 0.080 & 0.107 & 0.909 \\ ru & Indo-European (Balto-Slavic) & 0.046 & 0.075 & 1.769 & 0.129 & 0.122 & 1.077 \\ tr & Altaic (Turkic) & 0.042 & 0.071 & 1.917 & 0.033 & 0.057 & 1.500 \\ \hline
**XLM-R + R-KAT** & & & & & & & \\ en & Indo-European (Germanic) & 0.449 & 0.376 & 0.412 & 0.628 & 0.600 & 0.531 \\ ar & Afro-Asiatic (Center-Semitic) & 0.332 & 0.361 & 0.529 & 0.407 & 0.400 & 0.824 \\ de & Indo-European (Germanic) & 0.343 & 0.354 & 1.077 & 0.333 & 0.162 & 1.308 \\ es & Indo-European (Romance) & 0.198 & 0.230 & 0.833 & 0.346 & 0.238 & 1.417 \\ fr & Indo-European (Romance) & 0.103 & 0.136 & 1.276 & 0.230 & 0.205 & 1.069 \\ nl & Indo-European (Germanic) & 0.300 & 0.214 & 1.455 & 0.225 & 0.244 & 1.273 \\ ru & Indo-European (Balto-Slavic) & 0.250 & 0.137 & 1.615 & 0.173 & 0.196 & 1.385 \\ tr & Altaic (Turkic) & 0.186 & 0.171 & 1.417 & 0.190 & 0.197 & 2.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of our model on some specific languages. have higher requirements for the model, which is conducive to improving the cross-lingual ability of the model. On the other hand, these data are also collected according to our standard procedures and are consistent with reality.