CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding

Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai, Yiqun Liu


Abstract
Legal case retrieval is a critical process for modern legal information systems. While recent studies have utilized pre-trained language models (PLMs) based on the general domain self-supervised pre-training paradigm to build models for legal case retrieval, there are limitations in using general domain PLMs as backbones. Specifically, these models may not fully capture the underlying legal features in legal case documents. To address this issue, we propose CaseEncoder, a legal document encoder that leverages fine-grained legal knowledge in both the data sampling and pre-training phases. In the data sampling phase, we enhance the quality of the training data by utilizing fine-grained law article information to guide the selection of positive and negative examples. In the pre-training phase, we design legal-specific pre-training tasks that align with the judging criteria of relevant legal cases. Based on these tasks, we introduce an innovative loss function called Biased Circle Loss to enhance the model’s ability to recognize case relevance in fine grains. Experimental results on multiple benchmarks demonstrate that CaseEncoder significantly outperforms both existing general pre-training models and legal-specific pre-training models in zero-shot legal case retrieval. The source code of CaseEncoder can be found at https://github.com/Anonymous-EMNLP2023/CaseEncoder.
Anthology ID:
2023.emnlp-main.441
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7134–7143
Language:
URL:
https://aclanthology.org/2023.emnlp-main.441
DOI:
10.18653/v1/2023.emnlp-main.441
Bibkey:
Cite (ACL):
Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai, and Yiqun Liu. 2023. CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7134–7143, Singapore. Association for Computational Linguistics.
Cite (Informal):
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding (Ma et al., EMNLP 2023)
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PDF:
https://aclanthology.org/2023.emnlp-main.441.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.441.mp4