@article{MENG2022193,
title = {Augmented and challenging datasets with multi-step reasoning and multi-span questions for Chinese judicial reading comprehension},
journal = {AI Open},
volume = {3},
pages = {193-199},
year = {2022},
issn = {2666-6510},
doi = {https://doi.org/10.1016/j.aiopen.2022.12.001},
url = {https://www.sciencedirect.com/science/article/pii/S2666651022000225},
author = {Qingye Meng and Ziyue Wang and Hang Chen and Xianzhen Luo and Baoxin Wang and Zhipeng Chen and Yiming Cui and Dayong Wu and Zhigang Chen and Shijin Wang},
keywords = {Dataset, Reading comprehension, Legal AI},
abstract = {The existing judicial reading comprehension datasets are relatively simple, and the answers to the questions can be obtained through single-step reasoning. However, the content of legal documents in actual scenarios is complex, making it problematic to infer correct results merely by single-step reasoning. To solve this type of issue, we promote the difficulties of questions included in Chinese Judicial Reading Comprehension (CJRC) dataset and propose two augmented versions, CJRC2.0 and CJRC3.0. These datasets are derived from Chinese judicial judgment documents in different fields and annotated by judicial professionals. Compared to CJRC, there are more types of judgment documents in the two datasets, and the questions become are more challenging to answer. For CJRC2.0, we only preserve complex questions that require to be solved by multi-step reasoning. Besides, we provide additional supporting facts to the answers. For CJRC3.0, we introduce a new question type, the multi-span question, which should be answered by extracting and combining multiple spans in the documents. We implement two powerful baselines to evaluate the difficulty of our proposed datasets. Our proposed datasets fill gaps in the field of explainable legal machine reading comprehension.}
}