## 1 Introduction
These studies convert the problem into a multi-label classification task by considering the problem only within a fixed set of articles in the limited scope of the case or law. The lack of training data and low accuracy are still issues despite the increase in expertise in this limited field. To overcome this problem, a study was conducted to re-rank the classification results by simultaneously using word-level and article-level features [6]. Through these studies, natural language processing could be developed as a legal search service for the relevant personnel and the general public. However, there is a disadvantage; it is a sentence prediction that uses legal expertise or a relatively simple classifier. In addition, when predicting the sentence, each task is handled independently. Recently, a study was able to predict the applied articles by employing a novel technique: the attention mechanism [7]. Judgments published by the Chinese court was used as training data for the neural network model. The top k articles were extracted by training the bi-directional gated recurrent unit (Bi-GRU) model [8] through the attention between the factual information recorded in the verdict and the existing candidate articles. When the target article containing the factual information of the verdict was added to the learning model, the accuracy was 95%; however, when it was excluded, the accuracy was approximately 80%. However, through an experiment, it was shown that performance improved when additional features were provided through the attention mechanism with candidate articles than when simple factual information was provided. An analysis of previous studies reveals that the factual information in a verdict is a shareable feature in terms of both the sentence and the article prediction. Furthermore, it can also be seen that the prediction performance can be improved through the attention mechanism. Therefore, in this study, an attention-mechanism-based multi-task learning \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Number of Marine Accidents** & **Number of Ships in Marine Accidents** & **Marine Accident Casualies** \\ \hline
2016 & 2307 & 2549 & 411 \\
2017 & 2582 & 2882 & 523 \\
2018 & 2671 & 2968 & 455 \\
2019 & 2971 & 3274 & 547 \\
2020 & 3156 & 3535 & 553 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall statistics of marine accidents from 2016 to 2020.  
model that shares factual information as input is presented as a solution. Multi-task learning is an approach where one neural network can perform several tasks simultaneously; it is an effective method when several problems share one feature [9]. By performing multiple tasks with one common feature, neural networks determine the appropriate output for each task. Therefore, it has the advantage of preventing the overfitting of one specific task [10]. The factual information of the verdict is used as an input for the shared layer and is delivered to three prediction tasks through the attention mechanism. The three prediction tasks are accident type, applied article, and sentence prediction. The attention mechanism is employed with sets of labels for each task. Through this process, each task can carefully capture the appropriate information from the shared features. In this study, we evaluate a multi-task learning model that predicts accident types, applied articles, and sentences for ship accidents in the Korean ocean. KMBT publishes a separate verdict for each ship accident that occurs on the Korean ocean. Since the verdict is structured data, factual explanations, applied articles, and sentences can be automatically extracted. After the shared layer automatically creates features from the extracted factual information, it delivers the appropriate features for the three tasks through the attention mechanism to perform the prediction tasks. The structure of this paper is as follows. Section 2 introduces the verdicts published by KMBT, the techniques used to construct the multi-task learning model, and the sentence prediction.