## 3 Methods
### Procedure
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Language-Special** & **Designed Template** & **Dev** & **Test1** & **Test2** & **Test3** \\ \hline \multirow{2}{*}{English} & T1(x) = The claim label may be [MASK] [MASK]. x. & 42.9 & 42.1 & 17.3 & 19.1 \\  & T2(x) = Maybe [MASK] [MASK]! Because of the claim that x. & 42.5 & 41.5 & 15.3 & 15.8 \\ \hline \multirow{2}{*}{Portuguese} & T1(x) = O rótulo da reivindicacao pode ser [MASCARA] [MASCARA]. x. & 40.3 & 40.5 & 15.8 & 12.8 \\  & T2(x) = Talvez [MASCARA] [MASCARA]! Por causa da aleegação de que x. & 40.1 & 40.4 & 16.0 & 12.9 \\ \hline \multirow{2}{*}{Indonesian} & T1(x) = Label kaim mungkin [MASK] [MASK]. x. & 40.0 & 40.1 & 17.5 & 15.3 \\  & T2(x) = Mungkin [MASK] [MASK]! Karena klam bahwa x. & 40.4 & 40.2 & 17.6 & 16.3 \\ \hline \multirow{2}{*}{Null-Prompt} & T(x) = [MASK] [MASK]. x & 41.1 & 43.4 & 17.4 & 14.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Micro F1 scores evaluated with the different prompt templates encoded by different languages. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Recursive** \\ **Layers** \\ \end{tabular} } & \multicolumn{4}{c}{**8-Head Attention Aggregator**} & \multicolumn{4}{c}{**16-Head Attention Aggregator**} \\ \cline{2-9}  & **Dev** & **Test1** & **Test2** & **Test3** & **Dev** & **Test1** & **Test2** & **Test3** \\ \hline
**0** & 36.4 & 36.1 & 15.9 & 13.8 & 36.4 & 36.1 & 15.9 & 13.8 \\
**1** & 42.6 & 39.1 & 16.9 & 18.1 & 36.5 & 37.5 & 16.6 & 19.0 \\
**2** & 42.9 & 42.1 & 17.3 & 19.1 & 40.4 & 38.9 & 16.7 & 15.5 \\
**3** & 42.8 & 38.0 & 15.2 & 17.1 & 40.6 & 36.6 & 16.0 & 15.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Micro F1 values on the different multi-head attention aggregators with different recursive layers (%). sufficient information to classify the claim. This result may be explained by the fact that the new title of partial search snippets is mainly extracted as evidence, whereby information is insufficient. Next, through the evidence source link provided in the dataset, we further find the truth evidence from the full text of the evidence web pages. As shown in Figure 3, the core part of the evidence that determines the veracity of the claim is briefly listed in the table. A comparison of the retrieval evidence and truth evidence confirms that the claim verification component can not make the right inference with insufficient evidence. To explore the effect of this issue, we test our models on an evidence-enhanced dev set, in which we add the multi-hop ground truth evidence chain using the full text of the

Figure 3: A typical error analysis example in which the true label is misleading, while the predicted label is the other. The claim and retrieved evidence, which is highlighted in red font, come from the training dataset, and part of the truth evidence, which is marked with red font and is underlined, are manually searched from the evidence source website. For reference, translations are also shown. In addition, the metadata are also listed for integrity-checking. evidence pages. The experimental results show that augmenting evidence significantly improves model performance, which further is in agreement with the idea that fine-grained evidence aggregating and reasoning can contribute to verifying the claim. In addition, the error case analysis in this study also enhances an understanding that leveraging prompt learning to directly verify the claim possibly is a better idea for fact-checking the retrieval evidence to verify whether the claim is true or false or is similar to the claim. Hypothetically, this means it is worthwhile to design a better evidence retrieval pipeline, and the models are able to ingest large documents (web pages); thus, their performance increase could have been much more significant, which remains to be our focus in future research. ## 5 Discussion

In this section, we will discuss results by answering a series of research questions. **Do the different pre-trained multilingual models make wide differences?