Court View Generation (CVG) is a challenging task in the field of Legal
Artificial Intelligence (LegalAI), which aims to generate court views based on
the plaintiff claims and the fact descriptions. While Pretrained Language
Models (PLMs) have showcased their prowess in natural language generation,
their application to the complex, knowledge-intensive domain of CVG often
reveals inherent limitations. In this paper, we present a novel approach, named
Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To
efficiently incorporate domain knowledge during the training stage, we
introduce a knowledge-injected prompt encoder for prompt tuning, thereby
reducing computational overhead. Moreover, to further enhance the model's
ability to utilize domain knowledge, we employ a generating navigator, which
dynamically guides the text generation process in the inference stage without
altering the model's architecture, making it readily transferable.
Comprehensive experiments on real-world data demonstrate the effectiveness of
our approach compared to several established baselines, especially in the
responsivity of claims, where it outperforms the best baseline by 11.87%.

Knowledge Injection and Guidance (KIG) is a novel approach that combines Pretrained Language Models (PLMs) with domain knowledge to enhance Court View Generation (CVG), outperforming established baselines in the responsivity of claims by 11.87%.

借助知识注入和指导增强法院视图生成

Enhancing Court View Generation with Knowledge Injection and Guidance

A basic task for most Legal Artificial Intelligence (Legal AI) applications
is Named Entity Recognition (NER). However, texts produced in the context of
legal practice make references to entities that are not trivially recognized by
the currently available NERs. There is a lack of categorization of legislation,
jurisprudence, evidence, penalties, the roles of people in a legal process
(judge, lawyer, victim, defendant, witness), types of locations (crime
location, defendant's address), etc. In this sense, there is still a need for a
robust golden collection, annotated with fine-grained entities of the legal
domain, and which covers various documents of a legal process, such as
petitions, inquiries, complaints, decisions and sentences. In this article, we
describe the development of the Golden Collection of the Brazilian Judiciary
(CDJUR-BR) contemplating a set of fine-grained named entities that have been
annotated by experts in legal documents. The creation of CDJUR-BR followed its
own methodology that aimed to attribute a character of comprehensiveness and
robustness. Together with the CDJUR-BR repository we provided a NER based on
the BERT model and trained with the CDJUR-BR, whose results indicated the
prevalence of the CDJUR-BR.

本文描述了巴西司法部的金标准收录（CDJUR-BR）的发展，其中包含了专家在法律文件中注释的一系列细粒度命名实体，并提供了一个基于 BERT 模型的 NER，其结果表明 CDJUR-BR 的优越性。

CDJUR-BR -- 巴西司法的黄金法律文档收藏，包含细粒度命名实体

CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice  with Fine-Grained Named Entities

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of
artificial intelligence, especially natural language processing, to benefit
tasks in the legal domain. In recent years, LegalAI has drawn increasing
attention rapidly from both AI researchers and legal professionals, as LegalAI
is beneficial to the legal system for liberating legal professionals from a
maze of paperwork. Legal professionals often think about how to solve tasks
from rule-based and symbol-based methods, while NLP researchers concentrate
more on data-driven and embedding methods. In this paper, we introduce the
history, the current state, and the future directions of research in LegalAI.
We illustrate the tasks from the perspectives of legal professionals and NLP
researchers and show several representative applications in LegalAI. We conduct
experiments and provide an in-depth analysis of the advantages and
disadvantages of existing works to explore possible future directions. You can
find the implementation of our work from this https URL

本文介绍了 Legal Artificial Intelligence 的历史，现状和未来方向，并从法律专业人员和自然语言处理研究人员的角度说明了任务，展示了几个代表性的应用程序，在现有工作的优缺点方面进行了深入的分析，探索了可能的未来方向。