Extracting Crime Prosecution Elements based on Neural Machine Reading Comprehension Model
- Resource Type
- Conference
- Authors
- Tsou, Jui-Ching; Hsieh, Kai-Yu; Huang, Chen-Hua; Shih, Yu-An; Yu, Han-Cheng; Fan, Yao-Chung
- Source
- 2022 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2022 IEEE International Conference on. :3329-3335 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Performance evaluation
Codes
Big Data
Forgery
Data models
Fraud
Data mining
Legal Document
NLP
Machine Reading Comprehension
- Language
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