Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents
NeuralMind Inteligência Artificial（NeuralMind 人工智能公司）；Institute of Computing, University of Campinas（坎皮纳斯大学计算研究所）；School of Electrical and Computer Engineering, University of Campinas（坎皮纳斯大学电气与计算机工程学院）
Ramon Pires, Fábio C. de Souza, Guilherme Rosa, Roberto A. Lotufo, Rodrigo Nogueira
A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possible introduction of bugs. In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods for information extraction of legal and registration documents. We finetune models that jointly extract the information and generate the output already in a structured format. Post-processing steps are learned during training, thus eliminating the need for rule-based methods and simplifying the pipeline. Furthermore, we propose a novel method to align the output with the input text, thus facilitating system inspection and auditing. Our experiments on four real-world datasets show that the proposed method is an alternative to classical pipelines.
EkStep Foundation（EkStep基金会）；Thoughtworks Technologies India Pvt Ltd.（Thoughtworks技术印度私人有限公司）；Agami
Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.
The impact of fine tuning in LLaMA on hallucinations for named entity extraction in legal documentation
FaCENA, Universidad Nacional del Nordeste（法塞纳国立东北大学）；Legalhub S. A.（Legalhub公司）；Instituto de Modelado e Innovación Tecnológica, CONICET（模型与技术创新研究所，CONICET）
Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）；Vellore Institute of Technology（韦洛尔理工学院）
This paper describes our submission to the Competition on Legal Information Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a new case and extracting supporting cases from the provided case law corpus to support the decision. Task 2 is the legal case entailment task, which involves the identification of a paragraph from existing cases that entails the decision in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec for semantic understanding and the traditional retrie…
A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
The University of Sussex（萨塞克斯大学）；Maastricht University（马斯特里赫特大学）；Massachusetts Institute of Technology（麻省理工学院）；VERSES Research Lab（VERSES 研究实验室）；Macquarie University（麦考瑞大学）；Université du Québec à Montréal（蒙特利尔魁北克大学）；University College London（伦敦大学学院）
Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies. We showcase the enhancement to this task brought by utilizing the knowledge graph that captures entity relations and their at…
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）
With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addict…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Current language understanding approaches focus on small documents, such as newswire articles, blog posts, product reviews and discussion forum entries. Understanding and extracting information from large documents like legal briefs, proposals, technical manuals and research articles is still a challenging task. We describe a framework that can analyze a large document and help people to know where a particular information is in that document. We aim to automatically identify and classify semantic sections of documents and assign consistent and human-understandable labels to similar sections a…
INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges
University of Campinas（坎皮纳斯大学）；National Center for State Courts (NCSC)（国家州法院中心）；Brazilian Association of Jurimetrics (ABJ)（巴西司法计量协会）；Terranova Consultoria（Terranova咨询公司）；Brazilian Federal Court of Accounts (TCU)（巴西联邦审计法院）
GeGS-PCR: Effective and Robust 3D Point Cloud Registration with Two-Stage Color-Enhanced Geometric-3DGS Fusion
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家级重点实验室）；National Engineering Research Center of Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；Xi’an Jiaotong University（西安交通大学）
Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models
Harbin Institute of Technology（哈尔滨工业大学）；Kuaishou Technology（快手科技）；Meta AI；Monash University（墨尔本大学）；Fudan University（复旦大学）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
Shandong University（山东大学）；Alibaba Group（阿里巴巴集团）；Indiana University Bloomington（印第安纳大学伯明顿分校）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize Research（阿霍德德尔海兹研究院）
Contract element extraction (CEE) is the novel task of automatically identifying and extracting legally relevant elements such as contract dates, payments, and legislation references from contracts. Automatic methods for this task view it as a sequence labeling problem and dramatically reduce human labor. However, as contract genres and element types may vary widely, a significant challenge for this sequence labeling task is how to transfer knowledge from one domain to another, i.e., cross-domain CEE. Cross-domain CEE differs from cross-domain named entity recognition (NER) in two important wa…
Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal
Faculty of Law, University of Cambridge（剑桥大学法学院）；The Psychometrics Centre, Cambridge Judge Business School, University of Cambridge（剑桥大学心理学测量中心，剑桥Judge商学院）；Faculty of English, University of Cambridge（剑桥大学英语学院）；Department of Statistics, Uppsala University（乌普萨拉大学统计系）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Engineering, University of Cambridge（剑桥大学工程系）
