One Size Does Not Fit All: The Case for Personalised Word Complexity Models
Sian Gooding, Manuel Tragut · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand.It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling.However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience.In this paper, we show that personal models are best when predicting word complexity for individual readers.We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.1