TY - GEN
T1 - What Makes a Charge? Identifying Charge-Discriminative Facts with Legal Elements
AU - Luo, Xiyue
AU - Chao, Wenhan
AU - Zhou, Xian
AU - Wang, Lihong
AU - Luo, Zhunchen
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Over the past few years, there has been a significant surge in the use of deep learning models for automated charge identification tasks, yielding impressive results. However, these models still face several limitations when it comes to explaining the rationale behind charge predictions. Recent research has sought to enhance interpretability by extracting discriminative features from factual descriptions. Nevertheless, such studies often neglect a thorough examination of the corresponding legal provisions. To tackle this issue, the objective of this paper is to establish a fine-grained connection between charges and factual components, specifically identifying charge-discriminative facts that encompass essential legal elements. Several challenges need to be addressed, including the presence of noisy sentences, imbalanced data, and the need for distinct legal elements corresponding to different charges. To tackle these challenges, this paper reframes the task as a few-shot text classification problem and introduces a meta-learning framework that integrates legal element information through a prototypical network. The experimental results demonstrate the effectiveness of this approach in improving charge prediction accuracy and establishing meaningful associations between charges and factual evidences.
AB - Over the past few years, there has been a significant surge in the use of deep learning models for automated charge identification tasks, yielding impressive results. However, these models still face several limitations when it comes to explaining the rationale behind charge predictions. Recent research has sought to enhance interpretability by extracting discriminative features from factual descriptions. Nevertheless, such studies often neglect a thorough examination of the corresponding legal provisions. To tackle this issue, the objective of this paper is to establish a fine-grained connection between charges and factual components, specifically identifying charge-discriminative facts that encompass essential legal elements. Several challenges need to be addressed, including the presence of noisy sentences, imbalanced data, and the need for distinct legal elements corresponding to different charges. To tackle these challenges, this paper reframes the task as a few-shot text classification problem and introduces a meta-learning framework that integrates legal element information through a prototypical network. The experimental results demonstrate the effectiveness of this approach in improving charge prediction accuracy and establishing meaningful associations between charges and factual evidences.
KW - Charge-discriminative fact identification
KW - Few-shot text classification
KW - Meta-learning framework
UR - https://www.scopus.com/pages/publications/85174707010
U2 - 10.1007/978-3-031-44693-1_24
DO - 10.1007/978-3-031-44693-1_24
M3 - 会议稿件
AN - SCOPUS:85174707010
SN - 9783031446924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 298
EP - 310
BT - Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings
A2 - Liu, Fei
A2 - Duan, Nan
A2 - Xu, Qingting
A2 - Hong, Yu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023
Y2 - 12 October 2023 through 15 October 2023
ER -