# Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination
## 4 Experiments
### Comparison with the State-of-the-Art Model of Relation Extraction  
In this part, we compare SSPC with several recent relation extraction models using prompts. Moreover, we validate the model's capabilities in two settings, including standard supervised training and few-shot learning.  
* **ENT MARKER and TYP MARKER**: [39] uses prompt tuning for relation extraction. ENT MARKER injects special symbols to index the positions of entities. It is similar to prompting by introducing extra serial information to indicate the position of special tokens, i.e., named entities. TYP MARKER additionally introduces the type information of entities. It could be regarded as a type of template for prompts but requires additional annotation of type information.
* **PTR**: [3] proposes prompt tuning with rules (PTR) for many-class text classification and applies logic rules to construct prompts with several sub-prompts.
* **KnowPrompt**: [35] incorporates knowledge among relation labels into prompt-tuning for relation extraction and proposes a knowledge-aware prompt-tuning approach with synergistic optimization. It is the current state-of-the-art model on TACTED and TACREV in few-shot learning scenarios.  
Table 4 shows our experimental results. The first column is the names of the baseline models. We use micro-averaged \(F1\) (excluding \"no_relation\" type) for evaluation and treat a \(\langle S,R,O\rangle\) as correct when \(S\) and \(O\)'s boundaries are correct. From the results, we can see that our model performs well. In the standard supervised setting, \(F1\) increases by 0.3% on the TACRED dataset and 0.7% on the TACREV dataset. In the few-shot setting, \(F1\) increases by 0.3% on the TACRED dataset and 1.6% on the TACREV dataset on average. Note that when \(K\) is set to 32, the effect is the best, and \(F1\) increases by 3.0%. The experimental results prove that SSPC has fully adapted to the relation extraction task and is very effective in few-shot learning scenarios. Without introducing additional knowledge, SSPC still outperforms other robust baseline models. We owe these performance increases to our sub-prompts combination method.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Entity**} & \multicolumn{3}{c}{**Relation**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{ADE} & CNN + Global features [16] & 79.50 & 79.60 & 79.50 & 64.00 & 62.90 & 63.40 \\  & BiLSTM + SDP [17] & 82.70 & 86.70 & 84.60 & 67.50 & 75.80 & 71.40 \\  & Multi-head [18] & 84.72 & 88.16 & 86.40 & 72.10 & 77.24 & 74.58 \\  & Multi-head + AT [19] & - & - & 86.73 & - & - & 75.52 \\  & Relation-Metric [20] & 86.16 & 88.08 & 87.11 & 77.36 & 77.25 & 77.29 \\  & SpERT [10] & 88.99 & 89.59 & 89.28 & 77.77 & 79.96 & 78.84 \\  & SPAN [9] & 89.88 & 91.32 & 90.59 & 79.56 & 81.93 & 80.73 \\  & TANL [21] & - & - & 90.20 & - & - & 80.60 \\  & REBEL [22] & - & - & - & - & - & 81.70 \\  & SSPC (ours) & **90.66** & **91.32** & **90.99** & **80.79** & **82.86** & **81.79** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main experimental results on the ADE dataset. The best results are bold.