# Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination
## 4 Experiments
### Comparison with the State-of-the-Art Model of the ADE Dataset  
In this part, we compare SSPC with other joint entity relation extraction models, including the state-of-the-art model:  
* **Natural Language Understanding (NLU) models**, including \"CNN + Global features\" proposed by [16], \"BiLSTM + SDP\" proposed by [17], \"Multi-head\" proposed by [18], \"Multi-head + AI\" proposed by [19], \"Relation-Metric\" proposed by [20], \"SpERT\" proposed by [10], \"SPAN\" proposed by [9]. \"SpERT\" and \"SPAN\" are both span-based joint entity and relation extraction models. \"SpERT\" is a span-based joint entity and relation extraction with transformer pretraining model; \"SPAN\" is a span-based joint entity and relation extraction with attention-based span-specific and contextual semantic representations model and is the current span-based state-of-the-art model on the ADE dataset. * **Natural Language Generation (NLG) models**, including \"TANL\" proposed by [21] and \"REBEL\" proposed by [22]. Recently, the seq2 seq model not only performs well in language generation but also in NLU tasks. \"TANL\" is generative as it translates from an input to an output in augmented natural languages. \"REBEL\" is a seq2 seq model based on BART that performs end-to-end relation extraction and is the current state-of-the-art model on the ADE dataset. Table 3 shows our experimental results. The second column is the names of the baseline models. On the ADE dataset, we use standard _Precision_, _Recall_, and \(F1\) for evaluation. Furthermore, when \(S\) and \(O\)'s boundaries are correct, we treat a \(\langle S,R,O\rangle\) as correct. We \begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{1} & Adriamycin—induced cardiomyopathy aggravated by cis—platinum nephrotoxicity \\  & requiring dialysis. \\ \cline{2-3}  & \{ <cardiomyopathy, Adverse-Effect, Adriamycin\(>\), \\  & \(<\)enphrotoxicity, Adverse-Effect, cis—platinum\(>\) \} \\ \hline \multirow{3}{*}{2} & Possible interaction between lopinavir / ritonavir and valpro Acid exacerbates \\  & bipolar disorder. \\ \cline{2-3}  & \{ <bipolar disorder, Adverse-Effect, lopinavir, \\  & \(<\)bipolar disorder, Adverse-Effect, ritonavir, \\  & \(<\)bipolar disorder, Adverse-Effect, valpro Acid\(>\) \} \\ \hline \multirow{3}{*}{3} & Listeria brain abscess, Pneumocystis pneumonia and Kaposi ’s sarcoma \\  & after temozolomide. \\ \cline{2-3}  & \{ <Listeria brain abscess, Adverse-Effect, temozolomide\(>\), \\  & \(<\)Pneumocystis pneumonia, Adverse-Effect, temozolomide\(>\), \\  & \(<\)[PERSON]’s sarcoma, Adverse-Effect, temozolomide\(>\) \\ \hline \multirow{3}{*}{4} & Neutropenia and agranulocytosis are risks known to occur with phenothiazazines \\  & and clozapine. \\ \cline{2-3}  & \{ <Neutropenia, Adverse-Effect, phenothiazazines\(>\), \\  & \(<\)Neutropenia, Adverse-Effect, clozapine\(>\), \\  & \(<\)agranulocytosis, Adverse-Effect, phenothiazazines\(>\), \\  & \(<\)agranulocytosis, Adverse-Effect, clozapine\(>\) \} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Triples in the ADE dataset, including multiple triples (1), subjects and \(\langle R,O\rangle\) many-to-one (2), \(\langle S,R\rangle\) and objects one-to-many (3), and subjects and objects crossing (4). The subjects are marked in orange, and the objects are marked in blue. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Max epochs** & **Learning rate** & **Warm-up** & **Weight decay** & **Batch size** & **Time per epoch** \\ \hline
**ADE** & 20 & \(5\times 10^{-5}\) & 10\% & 0.01 & 1 & 46 min\(\times\) 10 \\
**TACRED** & 20 & \(3\times 10^{-5}\) & 10\% & 0.01 & 8 & 102 min \\
**TACREV** & 20 & \(3\times 10^{-5}\) & 10\% & 0.01 & 8 & 102 min \\
**TACRED**\({}^{\dagger}\) & 50 & \(3\times 10^{-5}\) & 10\% & 0.01 & 8 & 0.5–2 min \\
**TACREV**\({}^{\ddagger}\) & 50 & \(3\times 10^{-5}\) & 10\% & 0.01 & 8 & 0.5–2 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters for the different datasets.