## 3 Methods
### Procedure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Set Splitting**} & \multicolumn{3}{c}{**—Meta Data Augmentation**} & \multicolumn{3}{c}{**+Meta Data Augmentation**} \\ \cline{2-9}  & **Majority** & **Claim-Only** & **Attn-EA** & **Graph-EA** & **Claim-Only** & **Attn-EA** & **Graph-EA** & **Ours** \\ \hline
**Train** & 7.0 (\(\cdot\)) & 38.0 (0.8) & 40.2 (0.4) & 38.9 (0.2) & 39.8 (0.8) & 42.6 (1.1) & 40.1 (0.9) & **44.2 (1.2)** \\
**Dev** & 11.2 (\(\cdot\)) & 38.7 (0.6) & 40.6 (0.7) & 39.7 (0.5) & 39.6 (0.6) & 42.0 (0.4) & 40.0 (0.4) & **42.9 (0.6)** \\
**Test1** & 6.9 (\(\cdot\)) & 38.2 (0.9) & 38.9 (0.2) & 38.0 (0.9) & 39.4 (0.9) & 41.9 (1.2) & 39.2 (1.0) & **42.1 (0.7)** \\
**Test2** & 10.6 (\(\cdot\)) & 16.2 (0.9) & 15.7 (0.1) & 16.0 (0.9) & 15.4 (0.8) & 15.4 (1.5) & 15.6 (1.2) & **17.3 (1.9)** \\
**Test3** & 7.6 (\(\cdot\)) & 14.7 (0.6) & 16.5 (0.7) & 13.7 (0.6) & 16.7 (1.1) & 16.0 (0.3) & 15.0 (0.6) & **19.0 (1.1)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average F1 scores (standard deviations) evaluated in the experiences. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Dev** & **Test1** & **Test2** & **Test3** \\ \hline Ours & 42.9 & 42.1 & 17.3 & 19.1 \\ -UnifiedLossLayer & 39.2 & 39.0 & 16.3 & 15.9 \\ -FEATModule & 36.4 & 36.1 & 15.9 & 13.8 \\ -PCVModule & 38.0 & 37.9 & 15.2 & 17.1 \\ -LFEncoder & 35.9 & 39.1 & 17.4 & 17.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ablation test of our PEINet on each test. For the sake of brevity, the corresponding acronym abbreviation is adopted to indicate modules or layers. The main reason is that on one head, the English prompt templates are better in line with the claim label. On the other head, errors may exist in the translated verbalizer. For a sequence-special setting, the experimental results suggest that template T1 in typical narration outperforms template T2 in a flashback. ### Effectiveness of RGAT Parameters Setting

Next, we focus on the effect of the different parameters in the RGAT module. In our observation, from the results in Table 5, the 8-head attention aggregator model with two recursive layers achieves the best results, which indicates that claims from the obscure and complicated subset require multi-step evidence chain propagation. We further analyze the results from different angles. From the perspective of model layer numbers, the performance of a model with three recursive layers is inferior to that of a model with two recursive layers, which suggests that the model is overfitting with the increase in layers. On the other head, when analyzing the parameters of the attention heads, the results show that the performance of the 16-head attention aggregators is overall lower than that of the 8-head models, which suggests that the multi-head attention mechanism recreates the role of information aggregation, and the redundancy errors are gradually introduced with the increase in multi-attention heads. Regarding the whole analysis, the results demonstrate the ability of our model to verify the claims with recursive excavation and aggregation of evidence. ### Case Study and Error Analysis

In this section, we focus on the effect of error propagating based on experimental findings. We select representative samples from the train, dev, and test sets, which may reflect the prominent problem that PEINet confronts. Through our analysis findings of the typical error cases, the retrieved evidence--a set of homogeneous snippets to classify the claim--may be the primary factor leading to the misjudgment of the model, which is exacerbated by space-limited abbreviation writing. For example, in Figure 3, to verify the claim \"Wandering outside the home? Fire shoot in Malaysia with Drone.\", the pieces of evidence retrieved are almost indistinguishable--the #Evi1 and #Evi5 are almost similar, and the #Evi2, #Evi3, and #Evi4 are same. More critically, the evidence snippets are like replications of the claim, which cannot provide