## 3 Results
### Ablation Study on the Components of the Framework  
We remove the multi-turn vector resampling module to detect the contribution of this part. As shown in Table 3, all of the evaluation indexes, including precision, recall, and the F1 value, significantly decrease without this component, taking some irrelevant words into consideration. Since the change in the probability distribution occurs before the extraction stage, it acts on the union type and entity. In addition, the component transforms the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**NYT*} & \multicolumn{3}{c}{**WebNLG*} & \multicolumn{3}{c}{**NYT**} & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-13}
**Model** & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline EmRel (BERT) [38] & 91.7 & 92.5 & 92.1 & 92.7 & 93 & 92.9 & 92.6 & 92.7 & 92.6 & 90.2 & 87.4 & 88.7 \\ GRTE (BERT) [10] & 92.9 & 93.1 & 93 & 93.7 & 94.2 & 93.9 & 93.4 & 93.5 & 93.4 & 92.3 & 87.9 & 90 \\ ConCasRTE (BERT) [39] & 92.9 & 92.3 & 92.6 & 93.8 & 92.5 & 93.1 & 92.9 & 92.1 & 92.5 & 90.6 & 88.1 & 89.3 \\ BitKTE (BERT) [11] & 92.2 & 93.8 & 93 & 93.2 & 94 & 93.6 & 91.9 & 93.7 & 92.8 & 89 & 89.5 & 89.3 \\ CasRel (BERT) [40] & 89.7 & 89.5 & 89.6 & 93.4 & 90.1 & 91.8 & - & - & - & - & - & - \\ TP-Linker ([PERSON]) [41] & 91.3 & 92.5 & 91.9 & 91.8 & 92 & 91.9 & 91.4 & 92.6 & 92 & 88.9 & 84.5 & 86.7 \\ PRGC ([PERSON]) [42] & 93.3 & 91.9 & 92.6 & 94 & 92.1 & 93 & 93.5 & 91.9 & 92.7 & 89.9 & 87.2 & 88.5 \\ RFBFN ([PERSON]) [9] & 93.4 & 93.2 & 93.3 & 93.9 & 94.1 & 94 & 93.7 & 93.6 & 93.6 & 91.5 & 89.4 & 90.4 \\ RPSS ([PERSON]) [43] & 93.5 & 93.2 & 93.3 & 94.7 & 95.1 & 94.9 & - & - & - & - & - & - \\ [PERSON] ([PERSON]) [44] & 90 & 85.3 & 88 & 95.5 & 92.9 & 94.2 & 88.6 & 84.7 & 86.6 & 83.4 & 82.7 & 83 \\ BTDM ([PERSON]) [45] & 93 & 92.5 & 92.7 & 94.1 & 93.5 & 93.8 & 93.1 & 92.5 & 92.7 & 90.9 & 90.1 & 90.5 \\ SPN (BERT) [46] & 93.3 & 91.7 & 92.5 & 93.1 & 93.6 & 93.4 & - & - & - & - & - & - \\ NEDORT (BERT) [47] & 91.8 & 89.7 & 90.7 & 92.2 & 91.5 & 91.9 & - & - & - & - & - & - \\ DERP (BERT) [48] & 92.1 & 90 & 91 & 92.8 & 92.9 & 92.9 & - & - & - & - & - & - \\ ODRTEBERT [49] & 93.5 & 93.9 & 93.7 & 94.6 & 95.1 & 94.9 & 94.2 & 93.6 & 93.9 & 92.8 & 92.1 & 92.5 \\ SAFW (BERT) (Ours) & 94.4 & 94.6 & **94.5** & 96.4 & 96.3 & **96.3** & 94.4 & 95 & **94.7** & 91.9 & 92.1 & 92.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of SAFW with other baselines (all Rel-type results).  
output of the pre-trained model to a value more suitable for prediction, especially at a low learning rate. The results of the experiment prove that the multi-turn vector resampling module is effective and outperforms the direct use of the encoder output.  
Another improvement comes from the double cross attention layers, so we use single cross attention instead as a comparison. The F1 values drop with the recalls, because the design can mine more information from the encoded sequence. The input query may not be able to retrieve enough information from an interaction with it, because the information density of the former is lower than that of the latter. The candidates are from the description of the union types in the training datasets, and the vector sequences represent all of the sentences in the samples. Therefore, one time calculation may lose some important information for union-type finding or entity extraction in the fixed templates.