When instructors prepare learning materials for students, they frequently develop accompanying questions to guide learning. Natural language processing technology can be used to automatically generate such questions but techniques used have not fully leveraged semantic information contained in the learning materials or the full context in which the question generation task occurs. We introduce a sophisticated template-based approach that incorporates semantic role labels into a system that automatically generates natural language questions to support online learning. While we have not yet incorporated the full learning context into our approach, our preliminary evaluation and evaluation methodology indicate our approach is a promising one for supporting learning.
REF employed a template-based approach while taking advantage of semantic information to generate natural language questions for on-line learning support.
Generating Natural Language Questions to Support Learning On-Line
ENLG
Computer Science