JNLP Team: Deep Learning for Legal Processing in COLIEE 2020
Japan Advanced Institute of Science and Technology（北陆先端科学技术大学院大学）；VNU University of Engineering and Technology（河内国家大学工程技术大学）；Banking Academy of Vietnam（越南银行学院）；National Institute of Informatics（国立信息学研究所）
Ha-Thanh Nguyen, Hai-Yen Thi Vuong, Phuong Minh Nguyen, Binh Tran Dang, Quan Minh Bui, Sinh Trong Vu, Chau Minh Nguyen, Vu Tran, Ken Satoh, Minh Le Nguyen
We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve good performance, thus can be useful for tackling related problems in information retrieval, and decision support in the legal domain. Besides, the approach can be explored to deal with other domain specific problems.
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…
Government College of Engineering and Textile Technology, Berhampore（贝汉布尔政府工程与纺织技术学院）；Indian Institute of Technology, Patna（印度理工学院巴特那分校）
Natural Language Processing (NLP) and Information Retrieval (IR) in the judicial domain is an essential task. With the advent of availability domain-specific data in electronic form and aid of different Artificial intelligence (AI) technologies, automated language processing becomes more comfortable, and hence it becomes feasible for researchers and developers to provide various automated tools to the legal community to reduce human burden. The Competition on Legal Information Extraction/Entailment (COLIEE-2019) run in association with the International Conference on Artificial Intelligence an…
Japan Advanced Institute of Science and Technology（日本先端科学技术大学院大学）；University of Engineering and Technology, VNU（越南国立大学工程技术大学）；National Institute of Informatics（国立情报学研究所）
COLIEE is an annual competition in automatic computerized legal text processing. Automatic legal document processing is an ambitious goal, and the structure and semantics of the law are often far more complex than everyday language. In this article, we survey and report our methods and experimental results in using deep learning in legal document processing. The results show the difficulties as well as potentials in this family of approaches.
Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges that invite the use of semantic motivated NLP techniques. In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (CO…
Japan Advanced Institute of Science and Technology (JAIST)（日本先端科学技术大学院大学）；Hung Yen University of Education and Technology (UTEHY)（兴安教育技术大学）
In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Leg…
Japan Advanced Institute of Science and Technology（日本先进科学技术研究院）；National Institute of Informatics（国立信息学研究所）
We present our method for tackling the legal case retrieval task of the Competition on Legal Information Extraction/Entailment 2019. Our approach is based on the idea that summarization is important for retrieval. On one hand, we adopt a summarization based model called encoded summarization which encodes a given document into continuous vector space which embeds the summary properties of the document. We utilize the resource of COLIEE 2018 on which we train the document representation model. On the other hand, we extract lexical features on different parts of a given query and its candidates.…
Attentive Deep Neural Networks for Legal Document Retrieval
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；National Institute of Informatics（日本国立信息学研究所）；Posts and Telecommunications Institute of Technology（邮电技术学院）
NeuralMind；University of Campinas (Unicamp)（坎皮纳斯州立大学 (Unicamp)）；Federal University of Goiás (UFG)（戈亚斯联邦大学 (UFG)）；University of Waterloo（滑铁卢大学）
We describe our single submission to task 1 of COLIEE 2021. Our vanilla BM25 got second place, well above the median of submissions. Code is available at https://github.com/neuralmind-ai/coliee.
NOWJ@COLIEE 2026: Adaptive Pipelines for Legal Retrieval and Reasoning
VNU University of Engineering and Technology（越南国立工程技术大学）；Center for Juris-Informatics, ROIS-DS College of Engineering & Computer Science, VinUniversity（越南Vin大学工程与计算机科学学院法律信息学中心）
NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
VNU University of Engineering and Technology（越南工程大学）；Japan Advanced Institute of Science and Technology（日本先进科学研究院）；Center for Juris-Informatics, ROIS-DS（法律信息中心，ROIS-DS）；Research and Development Center for Large Language Models, NII（大型语言模型研发中心，NII）
NeuralMind；University of Campinas (Unicamp)（坎皮纳斯大学）；Federal University of Goiás (UFG)（戈亚斯联邦大学）；University of Waterloo（滑铁卢大学）
There has been mounting evidence that pretrained language models fine-tuned on large and diverse supervised datasets can transfer well to a variety of out-of-domain tasks. In this work, we investigate this transfer ability to the legal domain. For that, we participated in the legal case entailment task of COLIEE 2021, in which we use such models with no adaptations to the target domain. Our submissions achieved the highest scores, surpassing the second-best team by more than six percentage points. Our experiments confirm a counter-intuitive result in the new paradigm of pretrained language mod…
NeuralMind；University of Campinas (UNICAMP)（坎皮纳斯大学）
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment with zero-shot models in the legal case entailment task of the COLIEE 2022 competition. Our experiments show that scaling the number of parameters in a language model improves the F1 score of our previous zero-shot result by more than 6 points, suggesting that stronger zero-shot capability may be a characteristic of larger models, at least for this task. Our 3B-parameter zero-shot model outperforms all models, inclu…
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；University of Engineering and Technology, VNU（越南国立大学工程技术大学）；National Institute of Informatics（国立信息学研究所）
Ambiguity is a characteristic of natural language, which makes expression ideas flexible. However, in a domain that requires accurate statements, it becomes a barrier. Specifically, a single word can have many meanings and multiple words can have the same meaning. When translating a text into a foreign language, the translator needs to determine the exact meaning of each element in the original sentence to produce the correct translation sentence. From that observation, in this paper, we propose ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information to reduce am…
Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；Institute of Statistical Mathematics（统计数学研究所）；University of Engineering and Technology, VNU（越南国立大学工程技术学院）；National Institute of Informatics（日本国立信息学研究所）
In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition. Automated processing of legal documents is a challenging task because of the characteristics of legal documents as well as the limitation of the amount of data. With our detailed experiments, we found that Transformer-based pretrained language models can perform well with automated legal text processing problems with appropriate approaches. We describe in detail the processing steps for each task such as problem formulation, data proce…
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
National Institute of Informatics（国立信息学研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Imperial College London（伦敦帝国理工学院）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）
