## 3 Experiments
### Experimental Results  
Table 1 shows the experimental results of the Korean semantic role labeling. The models in [27,28] used word embedding made with word2 vec and did not use syntactic information, such as dependency trees and end-to-end neural network models. Reference [29] applied a structural support vector machine (S-SVM) for semantic role labeling and utilized information, such as syntactic information and name entity recognition results. The experiment showed that when the BERT model was applied to Korean semantic role labeling, the F1 score was 83.62%, which was an improvement of approximately 4% in comparison with that of the previous study. This result seems to be an appropriate answer to the question we asked at the beginning of the paper, whether we can apply BERT to Korean, a low-resource language. So as to determine the effect of the two simple features proposed in this paper on Korean semantic role labeling, experiments were conducted by adding each of them to the BERT-BiLSTM-CRF model. The results showed that the F1 score improved by 1.33% when the semantic features were used and by 2.15% when the text features were added. Finally, when the two feature information parts were used together, our model showed an F1 score of 86.36%, ultimately yielding state-of-the-art performance on the Korean PropBank benchmark. The important reasons for this improvement can be that these features are applicable to various datasets, do not increase the structural complexity of the model, and are easy to apply to other language models.  
We conducted the experiments to confirm that the proposed features and models performed well in both Korean and English benchmarks. Table 2 shows the experimental results of English semantic role labeling on the CoNLL-2009 English benchmark. All models in [14,15] use neural networks by incorporating syntactic feature information, such as dependency trees. Reference [22] is a model in which BERT is applied to semantic role labeling, and the BiLSTM-RNN-MLP model is used in the fine-tuning step. The model in [22] and our model did not use syntactic features, unlike the [14,15] models. Unlike our  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Test F1** \\ \hline Stacked BiLSTM RNN CRF [27] & 78.57 \\ \hline Highway BiLSTM RNN CRF [28] & 78.84 \\ \hline S-SVM with syntactic information and extra knowledge [29] & 79.54 \\ \hline BERT-BiLSTM-RNN-CRF (our model) & \\ (Bert-base) & 83.62 \\  + Semantic features & 84.95 \\  + Text features & 85.77 \\  + Semantic features + Text features & 86.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: F1 on the Korean PropBank benchmark with gold predicates.  
model, the BiLSTM-RNN-MLP model does not use feature information. Results showed that the BERT-BiLSTM-RNN-CRF model with the proposed features showed the best F1 score. Through the model in [22] and the results of our model, we could achieve better performance than the existing models without syntax information. In addition, analogous to the Korean experiment, it was proved that the performance improved when each feature information was added to the English experiment. Finally, there was a higher performance improvement when applied to Korean (+2.74%) than when applied to the text features (+1.87%) in the English experiment, owing to the difference in the characteristics of English, which consists of words, and Korean, which consists of several morphemes. Therefore, text features play a significant role in Korean semantic role labeling.