Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

ABSTRACT: Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-s

SUBMITTER: Hommel BE 

PROVIDER: S-EPMC9166894 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature