  Most existing Legal Judgment Prediction (LJP) models focus on discovering the
legal triggers in the criminal fact description. However, in real-world
scenarios, a professional judge not only needs to assimilate the law case
experience that thrives on past sentenced legal judgments but also depends on
the professional legal grounded reasoning that learned from professional legal
knowledge. In this paper, we propose a LegalDuet model, which pretrains
language models to learn a tailored embedding space for making legal judgments.
It proposes a dual-view legal clue reasoning mechanism, which derives from two
reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments
according to the judgment experiences learned from analogy/confusing legal
cases; 2) Legal Ground Reasoning, which lies in matching the legal clues
between criminal cases and legal decisions. Our experiments show that LegalDuet
achieves state-of-the-art performance on the CAIL2018 dataset and outperforms
baselines with about 4% improvements on average. Our dual-view reasoning based
pretraining can capture critical legal clues to learn a tailored embedding
space to distinguish criminal cases. It reduces LegalDuet's uncertainty during
prediction and brings pretraining advances to the confusing/low frequent
charges. All codes are available at https://github.com/NEUIR/LegalDuet.
