Development of the architecture of a transformer-based neural network model to automate delivering judgments in bankruptcy cases

Autor: Pylov Petr, Maitak Roman, Dyagileva Anna, Protodyakonov Andrey
Jazyk: English<br />French
Rok vydání: 2023
Předmět:
Zdroj: E3S Web of Conferences, Vol 402, p 03034 (2023)
Druh dokumentu: article
ISSN: 2267-1242
DOI: 10.1051/e3sconf/202340203034
Popis: Delivering judgments is one of the brightest examples of solving a creative problem, which implies not only the analysis of data presented in natural language, but also the verification of the compliance of the input information with legal norms and rules. Automation of this process requires the creation of such a language model of machine learning that would allow processing natural language and delivering judgments based on the legal framework, thereby completely replacing the position of a judge. Serious functional requirements are imposed on such an intelligent system, which describe the system of constraints for the architecture of a machine learning model in a formalized mathematical language. This article is devoted to defining the rules for building an applied artificial intelligence model that would automate the process of delivering judgments in bankruptcy cases.
Databáze: Directory of Open Access Journals