Combining Similarity and Transformer Methods for Case Law Entailment

Rabelo, Juliano; Kim, Miyoung; Goebel, Randy

doi:10.1145/3322640.3326741

Cited by 26 publications

(18 citation statements)

References 21 publications

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“…Case-based reasoning has been approached with expert systems (Popp and Schlink, 1974;Hellawell, 1980;v. d. L. Gardner, 1983), high-level handannotated features (Ashley and Brüninghaus, 2009) and transformer-based models (Rabelo et al, 2019). Closest to our work is Saeidi et al (2018), where a dialog agent's task is to answer a user's question about a set of regulations.…”

Section: Related Workmentioning

confidence: 99%

Factoring Statutory Reasoning as Language Understanding Challenges

Holzenberger

Durme

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.

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Section: Related Workmentioning

confidence: 99%

Factoring Statutory Reasoning as Language Understanding Challenges

Holzenberger

Durme

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…Besides a study on Japanese legal term correction proposed by Yamakoshi et al [56], a few very recent works address sentence-pair classification problems in legal information retrieval and entailment scenarios. Rabelo et al [47] propose to combine similarity based features and BERT fine-tuned to the task of case law entailment on the data provided by the Competition on Legal Information Extraction/Entailment (COLIEE), where the input is an entailed fragment from a case coupled with a candidate entailing paragraph from a noticed case. Sanchez et al [50] employ BERT in its regression form to learn complex relevance criteria to support legal search over news articles.…”

Section: Related Workmentioning

confidence: 99%

“…Developing BERT models for legal texts has very recently attracted increased attention, mostly concerning classification problems (e.g., Rabelo et al 2019;Chalkidis et al 2019a;Sanchez et al 2020;Shao et al 2020;Chalkidis et al 2020;Yoshioka et al 2021;Nguyen et al 2021). Our research falls into this context, as we propose a BERT-based framework for law article retrieval based on civil-law-based corpora.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code

Tagarelli

Simeri

2021

Artif Intell Law

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Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.

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“…Decision Support Systems. Many NLP systems have been developed to process text data (such as records, reports, scientific papers, and social media posts) to assist in making highly critical decisions, in domains like healthcare (Bampa and Dalianis, 2020;Mascio et al, 2020;Feng et al, 2020;Proux et al, 2009), finance (Kogan et al, 2009;Wang et al, 2013), business and management (Dong and Wang, 2015;Assawinjaipetch et al, 2016;Filgueiras et al, 2019), and legislation (Rabelo et al, 2019;Soh et al, 2019;Shaffer and Mayhew, 2019). Our work proposes the first decision support system to process nursing/midwifery complaints.…”

Section: Related Workmentioning

confidence: 99%

Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies

Lertvittayakumjorn¹,

Petej²,

Gao³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals. A major task of such regulators is the investigation of complaints against practitioners. However, processing a complaint often lasts several months and is particularly costly. Hence, we worked with international regulators from different countries (the UK, US and Australia), to develop the first decision support tool that aims to help such regulators process complaints more efficiently. Our system uses state-of-the-art machine learning and natural language processing techniques to process complaints and predict their risk level. Our tool also provides additional useful information including explanations, to help the regulatory staff interpret the prediction results, and similar past cases as well as non-compliance to regulations, to support the decision making.

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Combining Similarity and Transformer Methods for Case Law Entailment

Cited by 26 publications

References 21 publications

Factoring Statutory Reasoning as Language Understanding Challenges

Factoring Statutory Reasoning as Language Understanding Challenges

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code

Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies

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