Extracting Crime Prosecution Elements based on Neural Machine Reading Comprehension Model

Jui-Ching Tsou, Kai-Yu Hsieh, Chen-Hua Huang, Yu-An Shih, Han-Cheng Yu, Yao-Chung Fan

Abstract: In this paper, we explore the task of extracting prosecution elements (text description about prosecution elements) in an indictment. We approach the prosecution element extraction problem by formulating it as a reading comprehension task. Specifically, our idea is to train a reading comprehension model to extract a text span to indicate the statement of a crime element according to an asked question. By such a reformulation, we leverage the power of neural machine reading models to prosecution element extraction task. Experimental evaluation demonstrates the feasibility of the machine reading reformulation. We also make our code and data available on https://github.com/NCHU-NLP-Lab/Legal-Document-Question-Answering