LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning

• URL: http://arxiv.org/abs/2401.15371v3
• Date: Sat, 18 Jan 2025 01:19:52 GMT
• Title: LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning
• Authors: Pengjie Liu, Zhenghao Liu, Xiaoyuan Yi, Liner Yang, Shuo Wang, Yu Gu, Ge Yu, Xing Xie, Shuang-hua Yang,
• Abstract summary: We propose a LegalDuet model, which pretrains language models to learn a tailored embedding space for making legal judgments. Our experiments show that LegalDuet achieves state-of-the-art performance on the CAIL2018 dataset.
• Score: 38.94741836781938
• License:
• Abstract: 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.

Related papers

• Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning [12.589047235741194]
  We introduce LJPIV, the first benchmark dataset for Legal Judgment Prediction with Innocent Verdicts. Adhering to the trichotomous dogmatics, we extend three widely-used legal datasets through LLM-based augmentation and manual verification. Our experiments with state-of-the-art legal LLMs and novel strategies that integrate trichotomous reasoning into zero-shot prompting and fine-tuning reveal: (1) current legal LLMs have significant room for improvement, with even the best models achieving an F1 score of less than 0.3 on LJPIV.
  arXiv  Detail & Related papers  (2024-12-19T07:14:13Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Towards Explainability in Legal Outcome Prediction Models [64.00172507827499]
  We argue that precedent is a natural way of facilitating explainability for legal NLP models. By developing a taxonomy of legal precedent, we are able to compare human judges and neural models. We find that while the models learn to predict outcomes reasonably well, their use of precedent is unlike that of human judges.
  arXiv  Detail & Related papers  (2024-03-25T15:15:41Z)
• PILOT: Legal Case Outcome Prediction with Case Law [43.680862577060765]
  We identify two unique challenges in making legal case outcome predictions with case law. First, it is crucial to identify relevant precedent cases that serve as fundamental evidence for judges during decision-making. Second, it is necessary to consider the evolution of legal principles over time, as early cases may adhere to different legal contexts.
  arXiv  Detail & Related papers  (2024-01-28T21:18:05Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction [46.71918729837462]
  Given the fact description text of a legal case, legal judgment prediction aims to predict the case's charge, law article and penalty term. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss. We propose a moco-based supervised contrastive learning to learn distinguishable representations. We further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model.
  arXiv  Detail & Related papers  (2022-11-15T15:53:56Z)
• Legal Judgment Prediction (LJP) Amid the Advent of Autonomous AI Legal Reasoning [0.0]
  Legal Judgment Prediction is a longstanding and open topic in the theory and practice-of-law. Various methods and techniques to predict legal cases and judicial actions have emerged over time. The advent of AI Legal Reasoning will have a pronounced impact on how LJP is performed and its predictive accuracy.
  arXiv  Detail & Related papers  (2020-09-29T00:12:42Z)
• Distinguish Confusing Law Articles for Legal Judgment Prediction [30.083642130015317]
  Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts. We present an end-to-end model, LADAN, to solve the task of LJP.
  arXiv  Detail & Related papers  (2020-04-06T11:09:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.