# FREDA: Few-Shot Relation Extraction Based on Data Augmentation
## 4 Experiment
### Analysis of Results  
In order to show more clearly that our model has a more fantastic boost in the CP-based encoder module, we show it in the line graph shown in Figure 6. In the figure, we can see that our model RTFRE (CP) effect is significantly better than the other models in all four few-shot settings, which shows the superiority of our model. However, there is a shortcoming in Table 3, where our model is lower than model SimpleFSRE at 10-way-5-shot in the encoder model for BERT-based, although the overall lift is higher. Our analysis suggests that our model is better suited for states with fewer K values, and the smaller experimental result of the boost from the 5-way-5-shot setting could verify our suspicions. Moreover, because of our lack of equipment, our batch was changed to 2 at the 10-way-5-shot, and the minor tuning of the parameters could also be an influence. However, our model test results have a greater boost than our baseline. Figure 5: Experimental results on the accuracy of the FewRel2.0 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{2}{*}{CNN} & Proto-HATT & 72.65/74.52 & 86.15/88.40 & 60.13/62.38 & 76.20/80.45 \\  & MLMAN & 75.01/ — & 87.09/90.12 & 62.48/ — & 77.50/83.05 \\ \hline \multirow{6}{*}{BERT} & BERT-PAIR & 85.66/88.32 & 89.48/93.22 & 76.84/80.63 & 81.76/87.02 \\  & Proto-BERT & 84.77/89.33 & 89.54/94.13 & 76.85/83.41 & 83.42/90.25 \\  & REGRAB & 87.95/90.30 & 92.54/94.25 & 80.26/84.09 & 86.72/89.93 \\  & TD-proto & —/84.76 & —/92.38 & —/74.32 & —/85.92 \\  & CTEG & 84.72/88.11 & 92.52/95.25 & 76.01/81.29 & 84.89/91.33 \\  & ConceptFERE & —/98.21 & —/90.34 & —/75.72 & —/81.82 \\  & HCRP & 90.90/93.76 & 93.22/95.66 & 84.11/89.95 & 87.79/92.10 \\  & SimpleFSRE & 91.29/94.42 & 94.05/96.37 & 86.09/90.73 & 89.68/**93.47** \\  & FREDA & 92.23/**94.86** & 94.59/**96.52** & 85.99/**91.66** & 90.40/93.18 \\  & MTB & —/91.10 & —/95.40 & —/84.30 & —/91.80 \\  & CP & —/95.10 & —/97.10 & —/91.20 & —/94.70 \\  & MapRE & —/95.73 & —/97.84 & —/93.18 & —/95.64 \\  & HCRP (CP) & 94.10/96.42 & 96.05/97.96 & 89.13/93.97 & 93.10/96.46 \\  & SimpleFSRE (CP) & 96.21/96.63 & 97.07/97.93 & 93.38/94.94 & 95.11/96.39 \\  & FREDA (CP) & 95.66/**97.26** & 97.31/**98.30** & 91.81/**95.32** & 95.26/**96.68** \\ \hline \multirow{2}{*}{\(\Delta\)} & \multirow{2}{*}{+5.09} & \multirow{2}{*}{+2.39} & \multirow{2}{*}{+8.25} & \multirow{2}{*}{+2.93} \\  & & & & & \\ \end{tabular}
\end{table} 
Table 3: The results of FSRE accuracy experiments on the FewRel1.0 validation/test set. The top-to-bottom encoder includes the use of CNN, the original BERT, and the addition of additional pretrained BERT. Where FREDA denotes the results of our implementation and the others are from results reported in papers or CodaLab. On the other hand, to demonstrate that our model yields better relation prototypes across relation classes and that our model is more aggregative when distinguishing between similar relations, we performed a visualization of relation instances on the validation set of the 5-way-1-shot setup. As shown in Figure 7, we conducted two experimental comparisons. Images a and c randomly select five relations, with the same color corresponding to the same relation between the two images. Image a visualizes the clustering of various relations in the dataset using the SimpleFSRE model, while image c visualizes the clustering of various relations in our model. These two images are used to compare the aggregation capabilities of our model and the SimpleFSRE model across different relations. Images b and d specify the extraction of five similar relations, with the same color indicating the same relation between the two images. Image b visualizes the clustering of similar relations in the dataset using the SimpleFSRE model, while image d visualizes the clustering of similar relations in our model.