## 5 Experimental Settings
### Main Results  
The authors of this paper conducted a comprehensive evaluation of SACT on both the FewRel 1.0 and FewRel 2.0 datasets, utilizing accuracy as the primary performance metric. The authors of this paper employed the traditional FSRE setting, specifically the N-way-K-shot setting, and the results are presented in Tables 3 and 4. Table 3 displays the outcomes of SACT on the validation and test sets of FewRel 1.0. In terms of the encoders, this paper includes models based on both CNN encoders and BERT encoders. For the part based on BERT encoders, the upper section corresponds to the BERT-based backend model, while the lower section corresponds to the CP-based backend model. Based on the data in Table 3, the authors of this paper draw the following conclusions:  
The BERT encoder exhibits greater competitiveness compared to the CNN encoder. Figure 7 illustrates the outcomes of the three CNN-based encoder models compared to SACT on FewRel 1.0. The figure distinctly shows that the precision of the models when using BERT as an encoder significantly outperforms the models using CNN as an encoder. In terms of the average accuracy, BERT-based models achieve an impressive 83.42%, while CNN-based models lag at 71.74%. This demonstrates the superiority of BERT as an instance encoder, as it can more effectively capture and represent the semantic information of instances. SACT has achieved significant improvements in few-shot relation extraction tasks. Figure 8 illustrates the comparative analysis between SACT and the suboptimal model (HCPR) on the FewRel 1.0 dataset under the 1-shot setting (specifically, 5-way-1-shot and 10-way-1-shot settings). As depicted in the diagram, SACT demonstrates notably high \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **5-Way-1-Shot** & **5-Way-5-Shot** & **10-Way-1-Shot** & **10-Way-5-Shot** \\ \hline Proto-CNN * & 35.09 & 49.37 & 22.98 & 35.22 \\ Proto-BERT * & 40.12 & 51.50 & 26.45 & 36.93 \\ BERT-PAIR * & 56.25 & 67.44 & 43.64 & 53.17 \\ Proto-CNN-ADV * & 42.21 & 58.71 & 28.91 & 44.35 \\ Proto-BERT-ADV * & 41.90 & 54.74 & 27.36 & 37.40 \\ HCRP & 76.34 & 83.03 & 63.77 & 72.94 \\ Ours (CP) & 81.28 & 88.92 & 68.18 & 79.03 \\ \hline \hline \end{tabular}  
* Representative results from FewRel rankings.  
\end{table} 
Table 4: Accuracy (%) of FSRE on the FewRel 2.0 domain adaptation test set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Encoder** & **Model** & **5-Way-1-Shot** & **5-Way-5-Shot** & **10-Way-1-Shot** & **10-Way-5-Shot** \\ \hline \multirow{3}{*}{CNN} & Proto-CNN & 72.65/74.52 & 86.15/88.40 & 60.13/62.38 & 76.20/80.45 \\  & Proto-HATT & 75.01/— & 87.09/90.12 & 62.48/— & 77.50/83.05 \\  & MLMAN & 79.01/— & 88.86/92.66 & 67.37/75.59 & 80.07/87.29 \\ \hline \multirow{7}{*}{BERT} & Proto-BERT & 84.77/89.33 & 89.54/94.13 & 76.85/83.41 & 83.42/90.25 \\  & TD-proto & —/84.76 & —/92.38 & —/47.32 & —/85.92 \\  & ConceptFERE & —/89.21 & —/90.34 & —/75.72 & —/81.82 \\  & DAPL & —/85.94 & —/94.28 & —/77.59 & —/89.26 \\  & HCRP (BERT) & 90.90/93.76 & 93.22/95.66 & 84.11/89.95 & 87.79/92.10 \\  & DRK & —/89.94 & —/92.42 & —/81.94 & —/85.23 \\  & SimpleFSRE & 91.29/94.42 & 94.05/96.37 & 86.09/90.73 & 89.68/93.47 \\  & Ours (BERT) & 92.31/94.83 & 94.05/97.07 & 86.92/90.46 & 89.36/93.65 \\ \hline \multirow{7}{*}{BERT} & CP & —/95.10 & —/97.10 & —/91.20 & —/94.70 \\  & MapReduce & —/95.73 & —/97.84 & —/93.18 & —/95.64 \\ \cline{1-1}  & HCRP (CP) & 94.10/96.42 & 96.05/97.96 & 89.13/93.97 & 93.10/96.46 \\ \cline{1-1}  & LPD & 93.51/95.12 & 94.33/95.79 & 87.77/90.73 & 89.19/92.15 \\ \cline{1-1}  & CBPM & —/90.89 & —/94.68 & —/82.54 & —/89.67 \\ \cline{1-1}  & Ours (CP) & 96.48/97.14 & 97.93/97.98 & 93.88/95.24 & 95.61/96.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy (%) of FSRE on the FewRel 1.0 validation/test set. As evident from the figures, this strategy confers substantial advantages to SACT when compared to other BERT-based and CP-based models.