Solving the Unsolvable: Translating Case Law in Hong Kong

• URL: http://arxiv.org/abs/2501.09444v2
• Date: Sat, 18 Jan 2025 13:32:15 GMT
• Title: Solving the Unsolvable: Translating Case Law in Hong Kong
• Authors: King-kui Sin, Xi Xuan, Chunyu Kit, Clara Ho-yan Chan, Honic Ho-kin Ip,
• Abstract summary: The paper critiques the governments and judiciarys sporadic and uncoordinated efforts to translate case law.<n>A proposed solution involves leveraging machine translation technology through a human-machine interactive translation platform.
• Score: 0.5605104491423386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the challenges translating case law under Hong Kong's bilingual legal system. It highlights the initial success of translating all written statutes into Chinese before the 1997 handover, a task mandated by the Basic Law. The effort involved significant collaboration among legal, linguistic, and translation experts, resulting in a comprehensive and culturally appropriate bilingual legal system. However, translating case law remains a significant challenge due to the sheer volume and continuous growth of judicial decisions. The paper critiques the governments and judiciarys sporadic and uncoordinated efforts to translate case law, contrasting it with the thorough approach previously taken for statute translation. Although the government acknowledges the importance of legal bilingualism, it lacks a sustainable strategy for translating case law. The Judiciarys position that translating all judgments is unnecessary, unrealistic, and not cost-effectiveis analyzed and critiqued for its impact on legal transparency and public trust. A proposed solution involves leveraging machine translation technology through a human-machine interactive translation platform, which undergoes two major transitions. Initially based on a neural model, the platform transitions to using a large language model for improved translation accuracy. Furthermore, it evolves from a single-agent system to a multi-agent system, incorporating Translator, Annotator, and Proofreader agents. This multi-agent approach, supported by a grant, aims to facilitate efficient, high-quality translation of judicial judgments by integrating advanced artificial intelligence and continuous feedback mechanisms, thus better meeting the needs of a bilingual legal system.

Related papers

• TransLaw: Benchmarking Large Language Models in Multi-Agent Simulation of the Collaborative Translation [1.112686067941444]
  TransLaw is a novel multi-agent framework implemented for real-world Hong Kong case law translation.<n>It employs three specialized agents, namely, Translator, Annotator, and Proofreader, to collaboratively produce translations for high accuracy in legal meaning.
  arXiv  Detail & Related papers  (2025-07-01T15:39:26Z)
• ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework [21.003203706712643]
  Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines.<n>Current datasets, derived from authentic cases, suffer from high human annotation costs and imbalanced distributions.<n>We propose an Adversarial Self-Play Lawyer Augmented Legal Judgment Framework, called ASP2LJ.<n>Our framework enables a judge to reference evolved lawyers' arguments, improving the objectivity, fairness, and rationality of judicial decisions.
  arXiv  Detail & Related papers  (2025-06-11T06:55:40Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases. Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models. Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• Automating Legal Concept Interpretation with LLMs: Retrieval, Generation, and Evaluation [27.345475442620746]
  Legal articles often include vague concepts for adapting to the ever-changing society. It requires meticulous and professional annotations and summarizations by legal experts. By emulating legal experts' doctrinal method, we introduce a novel framework, ATRIE. ATRIE comprises a legal concept interpreter and a legal concept interpretation evaluator.
  arXiv  Detail & Related papers  (2025-01-03T10:11:38Z)
• Hybrid Deep Learning for Legal Text Analysis: Predicting Punishment Durations in Indonesian Court Rulings [0.0]
  This study develops a deep learning-based predictive system for court sentence lengths. Our model, combining CNN and BiLSTM with attention mechanism, achieved an R-squared score of 0.5893.
  arXiv  Detail & Related papers  (2024-10-26T07:07:48Z)
• LLM-based Translation Inference with Iterative Bilingual Understanding [52.46978502902928]
  We propose a novel Iterative Bilingual Understanding Translation method based on the cross-lingual capabilities of large language models (LLMs)<n>The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately.<n>The proposed IBUT outperforms several strong comparison methods.
  arXiv  Detail & Related papers  (2024-10-16T13:21:46Z)
• DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model [16.63238943983347]
  DeliLaw is a Chinese legal counselling system based on a large language model. Users can consult professional legal questions, search for legal articles and relevant judgement cases, etc. on the DeliLaw system in a dialogue mode.
  arXiv  Detail & Related papers  (2024-08-01T07:54:52Z)
• (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts [52.18246881218829]
  We introduce a novel multi-agent framework based on large language models (LLMs) for literary translation, implemented as a company called TransAgents. To evaluate the effectiveness of our system, we propose two innovative evaluation strategies: Monolingual Human Preference (MHP) and Bilingual LLM Preference (BLP)
  arXiv  Detail & Related papers  (2024-05-20T05:55:08Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• 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)
• LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset [0.0]
  This study addresses the gap in the literature working with large legal corpora about how to isolate cases, in our case summary judgments, from a large corpus of UK court decisions. We use the Cambridge Law Corpus of 356,011 UK court decisions and determine that the large language model achieves a weighted F1 score of 0.94 versus 0.78 for keywords. We identify and extract 3,102 summary judgment cases, enabling us to map their distribution across various UK courts over a temporal span.
  arXiv  Detail & Related papers  (2024-03-04T10:13:30Z)
• Towards Grammatical Tagging for the Legal Language of Cybersecurity [0.0]
  Legal language can be understood as the language typically used by those engaged in the legal profession. Recent legislation on cybersecurity obviously uses legal language in writing. This paper faces the challenge of the essential interpretation of the legal language of cybersecurity.
  arXiv  Detail & Related papers  (2023-06-29T15:39:20Z)
• 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)
• Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses [0.0]
  This paper investigates the issue of correctly translating attributive clauses from Japanese to Chinese. A pre-edit scheme is proposed, which aims to enhance the accuracy of translation. A novel two-step prompt strategy is proposed, which has been demonstrated to improve the average translation accuracy score by over 35%.
  arXiv  Detail & Related papers  (2023-03-27T20:33:40Z)
• Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
  We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
  arXiv  Detail & Related papers  (2021-05-09T09:39:25Z)

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.