Progress in Natural Language Processing Technologies: Regulating Quality and Accessibility of Training Data

Abstract

Progress in natural language processing technologies (NLP) is a cardinal factor of major socioeconomic importance behind innovative digital products. However, inadequate legal regulation of quality and accessibility of training data is a major obstacle to this technological development. The paper is focused on regulatory issues affecting the quality and accessibility of data needed for language model training. In analyzing the normative barriers and proposing ways to remove them, the author of the paper argues for the need to develop a comprehensive regulatory system designed to ensure sustainable development of the technology.