One Size Does Not Fit All: The Case for Personalised Word Complexity Models
This work addresses the need for more accurate text simplification and readability tools by accounting for individual reader differences, though it is incremental as it builds on existing CWI systems.
The paper tackles the problem of Complex Word Identification (CWI) by demonstrating that personalized models outperform generic ones in predicting word complexity for individual readers, based on factors like first language and proficiency, and releases a dataset and models as a benchmark.
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.