## 4 Experiments
### Efficiency of the Model  
The model's number of parameters, training time, required memory for training, and inference time on the NYT dataset were evaluated, as shown in Table 8. Our model has slightly more parameters than TPLinker and OneRel, mainly due to the large number of parameters in the BiGRU model. In terms of training time, our model trained 1.5 times faster than TPLinker and 1.6 times faster than the OneRel model. We believe a possible reason for this is that our model is trained as a single module, allowing for batch processing of samples and faster tensor dot-product computation. Although OneRel is also trained as a single module, each linear layer requires a large number of parameters \(\left(n^{2}\times d_{n}\times 3\right)\) to calculate, where \(d_{n}\) is the embedding dimension of the encoder and n is the sentence length, resulting in more time consumption. TPLinker trains its head, tail, and relation separately as three models, which takes a relatively longer time. Regarding training memory, our tensor space was limited to a two-dimensional plane, requiring less memory. In terms of inference speed, TPLinker required the most time, mainly because it needs to iterate over all token pairs and use token linking to determine the head and tail entities. Therefore, it has the highest computational complexity, whereas our model and OneRel only need to iterate over  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**NYT***} & \multicolumn{4}{c}{**WebNLG***} \\ \cline{2-10}  & **Normal** & **SEO** & **EPO** & **SOO** & **Normal** & **SEO** & **EPO** & **SOO** \\ \hline CasRel & 87.3 & 91.4 & 92.0 & 77.0 & 89.4 & 92.2 & 94.7 & 90.4 \\ TPlinker & 90.1 & 93.4 & 94.0 & 90.1 & 87.9 & 92.5 & 95.3 & 86.0 \\ PRGC & 91.0 & 94.0 & 94.5 & 81.8 & 90.4 & 93.6 & 95.9 & 94.6 \\ GRTE & 91.1 & 94.4 & 95.0 & - & 90.6 & 94.5 & **96.0** & - \\ OneRel & 90.6 & 94.8 & 95.1 & 90.8 & **91.9** & **94.7** & 95.4 & 94.9 \\ Ours & **91.1** & **94.8** & **95.3** & **93.2** & 91.6 & 94.5 & 94.9 & **96.6** \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(F1\) values of different models for sentences with different overlapping patterns. The best is bold, and the next best is underlined.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**NYT***} & \multicolumn{4}{c}{**WebNLG***} \\ \cline{2-10}  & **N = 1** & **N = 2** & **N = 3** & **N = 4** & **N > 4** & **N = 1** & **N = 2** & **N = 3** & **N = 4** & **N > 4** \\ \hline CasRel & 88.2 & 90.3 & 91.9 & 94.2 & 83.7 & 89.3 & 90.8 & 94.2 & 92.4 & 90.9 \\ TPlinker & 90.0 & 92.8 & 93.1 & 96.1 & 90.0 & 88.0 & 90.1 & 94.6 & 93.3 & 91.6 \\ PRGC & 91.1 & 93.0 & 93.5 & 95.5 & 93.0 & 89.9 & 91.6 & 95.0 & 94.8 & 92.8 \\ GRTE & 90.8 & **93.7** & 94.4 & 96.2 & 93.4 & 90.6 & 92.5 & **96.5** & 95.5 & 94.4 \\ OneRel & 90.5 & 93.4 & 93.9 & **96.5** & 94.2 & 91.4 & 93.0 & 95.9 & **95.7** & 94.5 \\ Ours & **91.4** & 93.4 & **94.7** & 95.6 & **94.9** & **91.7** & **93.3** & 95.4 & 94.1 & **95.3** \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(F1\) values of different models for sentences with different numbers of triples. The best is bold, and the next best is underlined.  
tokens with specific markers. Our inference time was higher than that of OneRel, mainly because complete triplet decoding in OneRel usually only needs three labels, whereas our labels are relatively dense, requiring more iterations.