## 1 Introduction  
Semantic role labeling (SRL) is one of the fundamental tasks in natural language understanding (NLU). It aims to understand the predicate-argument structure of a sentence, to identify \"who did what to whom,\" \"when,\" \"where,\" etc. Given a sentence (in Figure 1), SRL has several subtasks in a sequential framework, including: predicate identification (make), predicate classification (make.02), argument identification (e.g., Vaccines), and argument classification. (Vaccines is A0 for the predicate make). This task finds its use in several downstream tasks, such as dialog systems [1], answering questions [2; 3], and extracting information [4; 5]. In recent years, breakthroughs achieved using end-to-end neural network models [6; 7; 8; 9] have resulted in a state-of-the-art SRL performance even without syntactic features. These performances appear to falsify the long-held belief that syntactic features are fundamental  
Figure 1: Example of a dependency-based SRL. prerequisites for performing SRL [10; 11]. However, state-of-the-art neural network models [12; 13; 14; 15] can produce results with even better accuracy by leveraging syntactic feature information. [PERSON] et al. [12] discussed the requirement of syntactic capabilities to achieve competitive performance in dependency-based SRL. [PERSON] et al. [13] showed that linguistic information, including syntactic feature information, can help improve SRL performance only when used adequately and efficiently on the SRL system during the training phase. Even in a previous study [7], it was shown that the use of gold syntax feature information could dramatically improve the SRL performance with no explicit linguistic features. Recently, the natural language processing (NLP) community has shown interest in neural models that heavily use pre-training based on language modeling [16; 17; 18]. Pre-training based on language models has shown impressive benefits in a variety of NLP tasks, from sentence classification to sequence labeling. Among these, the most recently developed bidirectional encoder representations from transformers (BERT) [18] was trained using the encoder part of the transformer model [19]; furthermore, it outperformed the other two language models. Most recently, in addition to the previously mentioned language models, there are language models that have trained more extensive parameters, such as XLNet [20] and GPT-3 [21]. However, many studies are still being conducted based on the BERT model because these models require very large computing power, even in inference time. Apart from the interest in the NLP community, there have been studies using the BERT model for semantic role labeling [22]. [PERSON] et al. [22] showed that the pre-trained BERT model performed sufficiently well in SRL tasks without any linguistic information. The semantic information of every word of the sentence is highly important in determining the meaning-role of the word, but to utilize any information from those words requires WordNet or a comparable thesaurus, thus limiting its practical usage. Therefore, in previous studies of semantic role labeling using machine learning, semantic information was excluded, and the study was primarily conducted using syntax analysis qualities or end-to-end neural network methods. Some common queries include: (1) Does BERT show good performance even when the model is applied to Korean, which is a low-resource language? (2) Can semantic information be obtained in a straightforward way? Can SRL be applied to the BERT-based semantic role-labeling model? (3) Will adding simple semantic information and text-originated feature information improve SRL performance? The answer to all such questions is positive.