## 3 Methods
### Procedure
Firstly, by vertically comparing model performance in training, validation, and testing, it can be seen that our model has no over-fitting. Next, by comparing the performance of the model on Test1 horizontally, the PEINet model outperforms the baseline models in the in-domain test, which demonstrates that the model has superior performance in multilingual automated fact-checking. Furthermore, our model significantly also outperforms previous methods not only on the Test2 set, but also on the Test3 set in further analysis, which means that our model has a better performance in both out-of-domain generalization, and zero-shot capabilities. The overall performance advantage of the model suggests that our motivation of the human fact-checking cognitive paradigm is valid and reasonable. In a separate analysis of the degree of performance superiority in three different test sets, our

model performs best in Test3, which further demonstrates the advantages of the model in few-/zero-shot scenarios. ### Ablation Study

To investigate the effect of each component in PEINet, a concise summary of ablation analysis by removing different modules of our model is shown in Table 3. We employ '-UnifiedLossLayer', '-FEAModule', '-PCVModule', and '-LFEncoderLayer', which, respectively, represents: unified multi-task loss function, fine-grained evidence aggregating module, prompt-based claim verification module, and LF-shared encoding module. Individually, '-UnifiedLossLayer' represents that the multi-task learning mechanism is not adopted, and both \(\lambda_{prompt}\) and \(\lambda_{tgt}\) are 0.5. Next, '-FEAModule' means that the recursive graph attention network is replaced by the three-layer fully connected neural network. Then, '-PCVModule' indicates that the prompt learning template is not employed, and the corresponding module is remodeled based on the claim embedding the special token [CLS]. Last, '-LFEncoder' represents that the language family feature is not encoded, and only other mete-data features are adopted to strengthen the model. The empirical results show that first, the removal of different components suffers different degrees of performance degradation. Second, the performance of the model without the fine-grained evidence aggregating module is subjected to a large reduction, which indicates that it is necessary to leverage the recursive graph attention between the claims and pieces of evidence to verify the claim, thus, contributing to boosting the final performance. Finally, without language family embedding, the model performance uniformly declined to a certain extent in each test scenario, which fully embodies the key role of language family metadata augmentation. ### Effectiveness of the Prompt Template

To verify the impact of different designed prompts, we manually design language-special and sequence-special contrast experiments. Therein, for language-special setting, except taking English as the prompt language, the Portuguese and Indonesian language are selected as representative languages, which belong to the top proportions of claim language. Furthermore, to evaluate whether there is an effective contribution of the language-special prompt, we also adopt the null prompt method, which simply concatenates inputs and the [MASK] tokens. As Table 4 shows, language-special prompts in correspondence with the source language of claim do not perform best in our experiments, and the null prompt also limits the performance. On the contrary, we observe that the language of prompts close to the source language of the claim label can effectively boost the model performance.