## 4 Experiments
### Experimental Results
#### 4.4.3 Detailed Results on Different Sub-Tasks  
The bidirectional translating decoding structure of our model can effectively mitigate error propagation and align entity pairs, and the linking of bipartite graphs can effectively identify the relations between entity pairs. To further investigate the performance of the model on the alignment of entity pair and identification of relations, we chose the current best SOTA model PRGC (one of the current state-of-the-art triple-extraction models, with an outstanding performance in relation judgement and subject-object alignment) for further comparison experiments on subtasks. The results are shown in Table 4.  
In general, BTDM outperforms PRGC in thirteen of the eighteen metrics and achieves a similar performance to PRGC in the remaining metrics. Moreover, BTDM exceeds PRGC in F1 metrics on all subtasks, indicating the overall superiority of model and the stability of performance of model on each subtask. In addition, on the relation extraction subtask, we perform about the same, or even slightly better, than the PRGC method on dataset NYT*. However, on dataset WebNLG with more types of relations, the recall rate is similar to that of PRGC: we achieved a 3.4% performance improvement in terms of precision, indicating the superior performance of the relation embedding and bipartite graph-linking approaches in relation extraction.  
Lastly, on the entity pair and relation triplet tasks, BTDM leads PRGC overall. The experiments also demonstrate that capturing the dependencies between entity pairs and relations is crucial, which points us in the direction of designing stronger models in future work.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{10}{c}{**NNT*} & \multicolumn{10}{c}{**WebNLG*} \\ \cline{2-13}  & **Normal** & **EPO** & **SLO** & **SOO** & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) & **Normal** & **EPO** & **SLO** & **SOO** & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) & \(N*\) \\ \hline \hline OrderCopyRE & 71.2 & 69.4 & 72.8 & - & 71.7 & 72.6 & 72.5 & 77.9 & 45.9 & 65.4 & 60.1 & 67.4 & - & 63.4 & 62.2 & 64.4 & 57.2 & 55.7 \\ \hline F1-Spinn & 88.5 & 67.6 & 60.3 & - & 88.5 & 82.1 & 74.7 & 75.6 & 76.9 & 87.3 & 91.5 & 80.5 & 82.1 & 86.5 & 91.4 & 89.5 & 91.1 \\ \hline Credit & 87.3 & 91.4 & 80.0 & 77.0 & 86.2 & 90.3 & 91.9 & 94.2 & 83.7 & 89.4 & 92.2 & 94.7 & 90.4 & 88.3 & 90.8 & 94.2 & 92.4 & 90.9 \\ \hline TFBLiner & 90.1 & 94.4 & 94.0 & **91.1** & 90.2 & 92.8 & 93.1 & 96.1 & 90.0 & 87.9 & 92.5 & 95.3 & 86.0 & 88.0 & 91.4 & 94.6 & 90.3 & 91.6 \\ PRGC & **91.0** & 94.0 & 94.5 & 91.8 & **91.1** & 90.0 & 93.5 & 95.3 & 90.4 & 90.6 & **95.8** & **94.6** & 98.9 & 91.6 & 90.0 & 94.8 & 92.8 \\ \hline BTDM & 90.8 & **94.7** & **94.9** & 87.5 & 90.7 & **90.4** & **94.2** & **96.2** & **94.0** & **91.0** & **94.3** & 93.5 & 94.6 & **90.8** & **92.5** & **96.1** & **95.4** & 92.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1-score (%) of relational triple-extraction with different overlapping patterns. Note that \(\uparrow\) marks results reported by PRGC, and **Bold** marks the highest score in the table.