Bringing order into the realm of Transformer-based language models for artificial intelligence and law
Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and ...
Lessons learned building a legal inference dataset
Legal inference is fundamental for building and verifying hypotheses in police investigations. In this study, we build a Natural Language Inference dataset in Korean for the legal domain, focusing on criminal court verdicts. We developed an ...
A topic discovery approach for unsupervised organization of legal document collections
Technology has substantially transformed the way legal services operate in many different countries. With a large and complex collection of digitized legal documents, the judiciary system worldwide presents a promising scenario for the development ...
Ant: a process aware annotation software for regulatory compliance
Accurate data annotation is essential to successfully implementing machine learning (ML) for regulatory compliance. Annotations allow organizations to train supervised ML algorithms and to adapt and audit the software they buy. The lack of ...
Multi-language transfer learning for low-resource legal case summarization
Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming ...
Automated legal reasoning with discretion to act using s(LAW)
Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable ...
