一句话：提出 TauJud，为司法文书生成按时间和地点均匀分布的测试数据，降低评估标注成本。
随着大数据在司法领域的普及，机器学习模型评估需要大量测试数据，但司法文书标注成本高昂。通用文本数据增强虽常用于提升分类性能，但判决文书要求可解释性与逻辑性，简单扩增并不适用。作者基于 CAIL2018 中超过 20 万份司法文书的观察，设计了测试增强工具 TauJud，旨在为模型评估生成更有效、在时间和地点上分布均匀的测试数据，并节省标注时间。该工具已在 GitHub 提供演示。
The booming of big data makes the adoption of machine learning ubiquitous in the legal field. As we all know, a large amount of test data can better reflect the performance of the model, so the test data must be naturally expanded. In order to solve the high cost problem of labeling data in natural language processing, people in the industry have improved the performance of text classification tasks through simple data amplification techniques. However, the data amplification requirements in the judgment documents are interpretable and logical, as observed from CAIL2018 test data with over 200,000 judicial documents. Therefore, we have designed a test augmentation tool called TauJud specifically for generating more effective test data with uniform distribution over time and location for model evaluation and save time in marking data. The demo can be found at https://github.com/governormars/TauJud.
DOI 原文 ·
@inproceedings{paperbot6064,
title = {TauJud: test augmentation of machine learning in judicial documents},
author = {Zichen Guo and Jiawei Liu and Tieke He and Zhuoyang Li and Peitian Zhangzhu},
booktitle = {ISSTA},
year = {2020},
doi = {10.1145/3395363.3404364}
}