## 4 Experiments
### Experimental Results
#### 4.4.2 Detailed Results on Complex Scenarios  
To validate the performance of BTDM in some complex scenarios, we used a subset of the NYT* and WebNLG* datasets for complementary experiments, which included  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NVT***} & \multicolumn{3}{c}{**WebNLG*} & \multicolumn{3}{c}{**NYT**} & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-13}  & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline NovelTagging [29] & - & - & - & - & - & - & 32.8 & 30.6 & 31.7 & 52.5 & 19.3 & 28.3 \\ CopyRE [30] & 61.0 & 56.6 & 58.7 & 37.7 & 36.4 & 37.1 & - & - & - & - & - & - \\ MultiGrad [31] & - & - & - & - & - & - & 60.7 & 58.6 & 59.6 & 57.5 & 54.1 & 55.7 \\ GraphRel [32] & 63.9 & 60.0 & 61.9 & 44.7 & 41.1 & 42.9 & - & - & - & - & - & - \\ OrderCopyRE [33] & 77.9 & 67.2 & 72.1 & 63.3 & 59.9 & 61.6 & - & - & - & - & - & - \\ ETL+span [24] & 84.9 & 72.3 & 78.1 & 84.0 & 91.5 & 87.6 & 85.5 & 71.7 & 78.0 & 84.3 & 82.0 & 83.1 \\ RSAN [34] & - & - & - & - & - & - & 85.7 & 83.6 & 84.6 & 80.5 & 83.8 & 82.1 \\ CasRel [17] & 89.7 & 89.5 & 89.6 & 93.4 & 90.1 & 91.8 & - & - & - & - & - & - \\ TPLinker [25] & 91.3 & **92.5** & 91.9 & 91.8 & 92.0 & 91.9 & 91.4 & **92.6** & 92.0 & 88.9 & 84.5 & 86.7 \\ TDEER [18] & 93.0 & 92.1 & 92.5 & 93.8 & 92.4 & 93.1 & - & - & - & - & - & - \\ PRGC [19] & **93.3** & 91.9 & 92.6 & 94.0 & 92.1 & 93.0 & **93.5** & 91.9 & **92.7** & 89.9 & 87.2 & 88.5 \\ R-BPtrNet [35] & 92.7 & **92.5** & 92.6 & 93.7 & 92.8 & 93.3 & - & - & - & - & - & - \\ \hline BTDM & 93.0 & **92.5** & **92.7** & **94.1** & **93.5** & **93.8** & 93.1 & 92.4 & **92.7** & **90.9** & **90.1** & **90.5** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison (%) of the proposed BTDM method with the baselines. Note **Bold** marks the highest score and underline marks the second best score.  
testing the capacity to handle different types of overlapping triples and sentences with varying amounts of triples. Following previous works [17; 19; 25; 34], We divided the overlapping patterns into four categories: _Normal, Single Entity Overlap (SEO), Entity Pair Overlap (EPO)_, and _Subject Object Overlap (SOO)_. The dataset can be divided into five subdatasets: \(N=1,N=2,N=3,N=4,N\geq 5\), where \(N\) is the number of relational triples in a sentence. We chose five strong models as our baselines, and Table 3 provides the total outcomes.  
As shown in the Table 3, BTDM has the highest F1-score on thirteen of the eighteen subsets and ranks second on the remaining four subsets. In addition, when there are multiple triples in a sentence, the BTDM obtains more performance gains.  
The excellent performance of BTDM could be due to two factors: on the one hand, it effectively alleviates the error propagation problem, ensuring the recall and precision of the extraction of entity pairs and further ensures the precision of the extracted relational triples; on the other hand, the model maintains a relation matrix for each relation and applies a specific relation link between each entity pair, which guarantees the recall of the extracted relational triples. This ultimately means that the model has an excellent composite F1 metric in various complex scenarios.  
In general, the aforementioned experimental results show that BTDM outperforms the baseline and is more robust in dealing with a variety of complicated scenarios.