Multi-Task Deep Learning Model with an Attention Mechanism for Ship Accident Sentence Prediction
Source abstract
Abstract
The number of ship accidents occurring in the Korean ocean has been steadily increasing year by year. The Korea Maritime Safety Tribunal (KMST) has published verdicts to ensure that the relevant personnel can share judgment on these accidents. As of 2020, there have been 3156 ship accidents; thus, it is difficult for the relevant personnel to study these various accidents by only reading the verdicts. Therefore, in this study, we propose a multi-task deep learning model with an attention mechanism for predicting the sentencing of ship accidents. The tasks are accident types, applied articles, and the sentencing of ship accidents. The proposed model was tested under verdicts published by the KMST between 2010 and 2019. Through experiments, we show that the proposed model can improve the performance of sentence prediction and can assist the relevant personnel to study these accidents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Law · Computational and Text Analysis Methods · Marine and Coastal Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering and Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University, Busan 49112, Korea
Department of Control & Automation Engineering and Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University, Busan 49112, Korea
References (20)
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