# Cross-Lingual Cross-Domain Transfer Learning for
## 6 Conclusions  
This study introduces an innovative method to utilize cross-lingual cross-domain transfer learning in a target task. We aim to implement contemporary machine learning models in Spanish, which lack annotated data for the specific tasks addressed in this study. Our strategy combines cascade-based multi-task transfer learning with mBERT-based word embeddings. This allows us to construct multiple pre-trained models using the available data and then adapt them to the domain in the target task. Our methodology is structured in four stages and can be systematically applied depending on the availability of annotated data. For practical application and validation of our method, we focus on rumor detection, using two auxiliary tasks: stance classification and bot detection. Our detailed experimental assessment explores 80 distinct configurations to discern the most effective way to utilize cross-lingual, cross-domain transfer learning across all tasks and datasets. The validation of our technique is supported by experimental results that demonstrate a marked enhancement in the target task. Impressively, our study reveals that the results for rumor detection in Spanish are promising, underscoring the benefits of using auxiliary tasks in English. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Set.** & **CLM** & **First Model** & **Second Model** & **Third Model** & **Macro-F1** \\ \hline
18 & CLM3 & Stance EN & – & – & 0.749 \\
43 & CLM8 & Stance EN & Bot EN & – & 0.764 \\
48 & CML9 & Stance EN & Bot ES & – & 0.752 \\
**73** & **CLM14** & **Stance EN** & **Bot EN** & **Bot ES** & **0.783** \\
78 & CLM15 & Stance EN & Bot ES & Bot EN & 0.729 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Intermediate results in the construction of the CLM14, for Spanish language. Figure 9: Comparison between fine-tuning and zero-shot. The differences observed for words and perplexity are statistically significant (according to the Kruskal–Wallis test, with a \(p\)-value \(<\) 0.05). However, this is not the case for sentences and token length, where no significant differences were found. Looking ahead, our research agenda includes exploring the integration of additional tasks, such as hate speech detection. We also want to compare our approach with multi-task joint learning frameworks to highlight differences from current cascade-based strategies. Furthermore, it is essential to assess the effectiveness of these methods on various social media platforms, to reduce the dependence on data obtained solely from Twitter (now X). Lastly, we are intrigued by the potential of applying the techniques outlined in this study to languages other than Spanish. Conceptualization, M.M. and E.P. ; methodology, E.P. ; software, E.P. ; validation, E.P. and M.M. ; resources, M.S. ; data curation, E.P. ; writing--original draft preparation, E.P. and M.M. ; writing--review and editing, M.M. and M.S. ; project administration, M.S. All authors have read and agreed to the published version of the manuscript. [PERSON] acknowledges funding support from the Millennium Institute for Foundational Research on Data (IMFD) (ANID--Millennium Science Initiative Program--Code ICN17_002) and the National Center of Artificial Intelligence (CENIA FB210017, Basal ANID). [PERSON] was also funded by the ANID grant FONDECYT 1241462. The founders played no role in the design of this study. The study was reviewed by the Research Ethics and Safety Unit of the Pontificia Universidad Catolica de Chile and was declared exempt from ethical and safety evaluation, since it did not investigate people or personal and/or sensitive data, nor did living beings or individuals participate in it. Materials, tangible or intangible, specially protected, will be used in scientific research and are not considered risky activities or agents that may put the subjects who participate, carry out, and/or intervene in the research and the environment at risk. The ethics committee certificate is included in the attached documents. Not applicable.