Year: 2025
Type: conference-paper
Abstract: Recent trends in NLP have shifted towards modeling lexical complexity as a continuous value, but practical implementations often remain binary.This opinion piece argues for the importance of truly graded lexical complexity prediction, particularly in language learning.We examine the evolution of lexical complexity modeling, highlighting the "data bottleneck" as a key obstacle.Overcoming this challenge can lead to significant benefits, such as enhanced personalization in language learning and improved text simplification.We call for a concerted effort from the research community to create high-quality, graded complexity datasets and to develop methods that fully leverage continuous complexity modeling, while addressing ethical considerations.By fully embracing the continuous nature of lexical complexity, we can develop more effective, inclusive, and personalized language technologies.
Author: David Alfter
Institution: University of Gothenburg
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412889248