## 3 Methods
### Procedure
The framework endeavored to solve three challenges: truth or falsehood transfer learning of claims in cross-language and cross-domain, prompt-based verification without sufficient evidence, and fine-grained evidence aggregation and inference for fact verification. As alluded to in our review of the whole work, our model first focuses on the shared encoding mechanism of the language family metadata to strengthen the interactive learning of claims in different languages. Then, the prompt-based checking and synchronous evidence inference verification for the claim furthermore bridge gaps in multilingual fact-checking. Finally, the unified multi-task learning framework adjusts and optimizes the proportion weight of each module. The experiments achieve significant improvements, which proves that our framework performs well. Further analysis demonstrates the robustness and superiority of our model in cross-lingual and cross-domain fact verification tasks on two competitive test datasets. In the future, we will further explore: (1) How to leverage common sense knowledge or prior knowledge of relevant claims for breaking through the limitations of insufficient evidence to aggregate the vital clues. (2) Adaptive prompt template designs for multilingual claim verification to maybe play a key role in heuristic fact-checking in few-shot or zero-shot scenarios. (3) Interpretable claim verification is a potential research direction, which requires easy-to-interpret evidence inference and traceability. Conceptualization, X.L. and W.W.; methodology, X.L. ; software, X.L. and W.W.; validation, X.L. and J.F. ; formal analysis, X.L. ; investigation, X.L. ; resources, X.L. and J.F. ; data curation, X.L., W.W. L.J. and C.L. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L., W.W., J.F., L.J., H.K. and C.L. ; visualization, X.L. and H.K. ; supervision, L.J. and C.L. ; project administration, X.L. and L.J. ; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (no. Y835120378). Not applicable. **Data Availability Statement:** [[https://github.com/LiXiaoyu0101/Multilingual-Fact-Checking](https://github.com/LiXiaoyu0101/Multilingual-Fact-Checking)]([https://github.com/LiXiaoyu0101/Multilingual-Fact-Checking](https://github.com/LiXiaoyu0101/Multilingual-Fact-Checking)) (publicly accessed on 1 September 2022). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Test1** & **Test2** & **Test3** \\ \hline \hline \multirow{3}{*}{**Claim-Only + Meta**} & \multicolumn{3}{c}{**X-FACT**} \\  & 39.4 (0.9) & 15.4 (0.8) & 16.7 (1.1) \\
**Attn-EA + Meta** & 41.9 (1.2) & 15.4 (1.5) & 16.0 (0.3) \\
**PEINet** & 42.1 (0.7) & 17.3 (1.9) & 19.0 (1.1) \\ \hline \hline \multicolumn{4}{c}{**X-FACT + ENGLISH**} \\
**Claim-Only + Meta** & 37.1 (2.7) & 14.5 (0.5) & 14.4 (0.3) \\
**Attn-EA + Meta** & 38.0 (4.5) & 14.7 (2.6) & 14.3 (1.9) \\
**PEINet** & 38.1 (4.9) & 17.0 (2.5) & 16.4 (1.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison analysis of different models with the augmenting data and original data. **Conflicts of Interest:** The authors declare that they do not have any conflicts of interest. This research does not involve any human or animal participation. All authors have checked and agreed with the submission. ## Appendix A Details on Dataset and Model

### Dataset Construction

The composition of the X-Fact dataset is detailed in Table 11. There are 25 diverse languages across 12 language families among the dataset, which is divided into training dataset, development dataset, and three different task-oriented testing datasets. Number 0 indicates not including the language, and number 1 indicates including the language. Moreover, the language code and fact-checker are displayed accordingly.