## 3 Results
### The Results of the Experiment  
Table 1 shows the results of SAFW on the scientific datasets. There are two types of experiments. Compared with the other baseline methods in the Rel+ mode, SAFW outperforms them. The result of Rel+ improves by 8.2% in SciERC and by 7.2% in Semeval 2017 task 10. Table 2 reports the results of SAFW on two basic datasets compared with the other baseline methods. SAFW improves by 0.8% F1 on NYT, 0.8% F1 on NYT*, and 1.4% F1 on WebNLG*. Note that the F1 values of NYT and WebNLG are over 90%, so our improvements show an effective promotion. All of these have proved the effectiveness of SAFW in performance improvement. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Extraction** \\ **Mode** \\ \end{tabular} & **Model Name** & **P** & **R** & **F** \\ \hline \multirow{8}{*}{Rel} & DyGE [27] & - & - & 41.6 \\  & SpERTPL [28] & 51.94 & 50.62 & 51.25 \\  & SpERTPL + OVO [39] & & & 52.6 \\  & cross-sentence ALB [30] & & & 50.1 \\  & PL-Marker [31] & & & 52 \\  & PL-Marker (Entity Neighbor) & & & 53.2 \\  & PL-Marker + OVO & & & 56.1 \\  & SpERT (SciBERT) [32] & 53.4 & 48.54 & 50.84 \\  & Flimf (SciBERT) [33] & 52.63 & 52.32 & 52.44 \\  & PURE (SciBERT) [28] & - & - & 50.1 \\  & [PERSON] ([PERSON]) + OVO & - & - & 51.6 \\  & PERA ([PERSON]) [22] & - & - & 55.3 \\  & [PERSON] [34] & - & - & 53.3 \\  & [PERSON] [35] & - & - & 55.7 \\  & ATG [36] & - & - & 51.1 \\  & SAFW ([PERSON]) & 53.1 & 46.3 & 49.5 \\ \hline \multirow{8}{*}{SciERC} & SpERTPL & 39.94 & 38.98 & 39.41 \\  & SpERTPL + OVO & & & 41.5 \\  & cross-sentence ALB & & & 36.7 \\  & PL-Marker & & & 40.6 \\  & PL-Marker (Entity Neighbor) & & & 41.6 \\  & PL-Marker + OVO & & & 44.5 \\  & [PERSON] ([PERSON]) & 36.25 & 40.04 & 38.05 \\  & [PERSON] ([PERSON]) & 42.27 & 39.01 & 40.58 \\  & [PERSON] ([PERSON]) & - & - & 36.8 \\  & [PERSON] ([PERSON]) + OVO & & & 40.1 \\  & PERA ([PERSON]) & - & - & 35.7 \\  & MCER & - & - & 42.8 \\  & MTL [37] & 44.33 & 35.46 & 39.66 \\  & HGERER & - & - & 43.6 \\  & ATG & - & - & 38.6 \\  & [PERSON] ([PERSON]) & 52.6 & 50.5 & 51.5 \\ \hline \multirow{8}{*}{Semeeval 2017 task 10} & [PERSON] ([PERSON]) & 46.8 & 30.5 & 36.9 \\  & [PERSON] ([PERSON]) & 56.52 & 31.1 & 40.12 \\  & [PERSON] ([PERSON]) & 52.86 & 35.41 & 42.41 \\  & [PERSON] ([PERSON]) & 42.6 & 39.7 & 41.1 \\ \hline \multirow{8}{*}{Semeeval 2017 task 10} & [PERSON] ([PERSON]) [23] & 40.4 & 21.2 & 27.8 \\  & DyGE (BERT) & 35.11 & 24.23 & 28.7 \\ \cline{1-1}  & SpERT (BERT) & 45.22 & 24.88 & 32.1 \\ \cline{1-1}  & Trimf (BERT) & 45.22 & 24.88 & 32.1 \\ \cline{1-1}  & [PERSON] (BERT) & 40.4 & 38.3 & **39.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of the two scientific datasets. We analyze the reasons for the improvement of \"Rel+\" and summarize two key points. First, the multiple rounds of resampling mines more accurate semantic information in the sequence encoded by the pre-trained encoding model and weakens the influence of irrelevant words on relation prediction. As relation prediction has priority over entity extraction, it is susceptible to the space of the result. The contribution ratio of several feature words is boosted by the re-sampling module, leading the model to find the correct relation types. Second, since the head and tail entity categories are encoded in common categories, the resulting template is set up to provide guidelines for both entity and relation categories. In the subsequent fusion process, the semantic information in the template is greatly enriched, due to the enhancing effect of the feature words, thus providing sufficient information to the relation quintuple. This kind of information is more explicit for type selection and entity extraction. Our SAFW combines the two improvements, in line with the end-to-end principle, so that we can achieve a better performance. In contrast, the other baselines do not consider reducing the result space by vector sampling in the encoding stage and reuse cross attention in mixed coding of hidden states.