Predicting Judicial Decisions of Criminal Cases from Thai Supreme Court Using Bi-directional GRU with Attention Mechanism

Abstract

Predicting court judgement has gained growing attention over the past years. Prior attempts used traditional prediction techniques based on Bag of words (BoW), where the order of words is discarded, resulting in low accuracy. In this paper, we propose a prediction model of criminal cases from Thai Supreme Court using End-to-End Deep Learning Neural Networks. Our model imitates a process of legal interpretation, whereby recurrent neural networks read the fact from an input case and compare them against relevant legal provisions with the attention mechanism. The model's output shows if a person is guilty of a crime according to the fact and laws. After the performance test, we find that our model could yield the higher F1 than traditional text classification techniques including Naive Bayes and SVM. In addition, we innovate the open dataset called “Thai Supreme Court Cases (TSCC)” that was compiled from many decades of Thai Supreme Court criminal judgements. It features the text of fact expertly extracted from each judgement, textual provisions from Thai Criminal Code, and binary-format labels following the theoretical criminal law structure. This dataset is useful for achieving judgement predicting task together with emulating actual criminal case trial.