## 4 Experimental Section
### Baseline Comparison Experiment  
To demonstrate the performance of REACT, we compared it with RSAN proposed by [PERSON] et al. [27], CasRel proposed by [PERSON] et al. [32], and TPLinker proposed by [PERSON] et al. [40] on the high-quality Chinese dataset we constructed. For the parameter settings of _Roberta_, we referred to the work of [PERSON] et al. [41]. For Roberta, [PERSON] et al. [41] conducted a large number of experiments in the field of Chinese natural language processing, achieving excellent results. Therefore, the parameter settings are relatively reliable, and the performance of the model can be guaranteed. As for _BiLSTM_, we conducted experiments for fine-tuning and selected the optimal parameters. The parameter settings for REACT are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Module** & **Parameter** & **Value** \\ \hline \hline \multirow{8}{*}{Roberta} & hidden\_size & 768 \\  & max\_position\_embedding & 512 \\  & num\_attention\_heads & 12 \\  & num\_hidden\_layers & 12 \\  & pooler\_fc\_size & 768 \\  & pooler\_num\_attention\_heads & 12 \\  & pooler\_num\_fc\_layers & 3 \\  & pooler\_size\_per\_head & 128 \\  & vocab\_size & 21,128 \\  & input\_size & 768 \\ \hline BiLSTM & hidden\_size & 64 \\ \hline LSTM & hidden\_size & 64 \\ \hline \multirow{4}{*}{CNN} & dropout & 0.4 \\ \cline{2-3}  & in\_channels & 768 \\ \cline{1-1}  & out\_channels & 128 \\ \cline{1-1}  & kernel\_size & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings for various parameters in REACT.  
Figure 10: Proportions of different entity types in the high-quality Chinese dataset.  
RSAN: [PERSON] et al. [27] hypothesized that words in a text have different degrees of importance under different relations. For this reason, they proposed a relation-based attention model, RSAN, which assigns different weights to contextual words under each relation. The reason for choosing this baseline for comparison is that the Relation Attention Mechanism in RSAN has some similarities with our proposed Entity Attention Mechanism.  
CasRel: [PERSON] et al. [32] adopted a new perspective instead of the previous classification mechanism, modeling the relation as a mapping from the head entity to the tail entity, and proposed a new cascading binary labeling framework (CaeRel). Excellent results were achieved due to the introduction of header entity information when identifying tail entities and relations. Since our model is an improvement on CasRel, a comparison with CasRel is necessary.  
TPLinker: In order to alleviate the exposure bias and error propagation issues in CasRel, [PERSON] et al. [40] proposed TPLinker. Joint extraction was described as a token-pair linking problem, and a new handshake labeling scheme was proposed to align entity pairs under each type of relation with boundary marking. This model ultimately achieved SOTA performance. Because our model is also an improvement on CasRel, it is necessary to compare REACT with TPLinker.  
The experimental results for this section are shown in Table 3 and Figure 11. It can be seen that CasRel, TPLinker, and REACT outperformed RSAN in the relation extraction task, with an improvement of about 10% in the F1-score. REACT outperformed CasRel and TPLinker. Therefore, we can conclude that by utilizing head entity information to reduce word noise instead of treating head entity information as a parameter for identifying tail entities and relations, we can achieve better performance.