One Size Does Not Fit All: The Case for Personalised Word Complexity Models
Computation and Language
2022-05-06 v1 Artificial Intelligence
Human-Computer Interaction
Machine Learning
Abstract
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.
Cite
@article{arxiv.2205.02564,
title = {One Size Does Not Fit All: The Case for Personalised Word Complexity Models},
author = {Sian Gooding and Manuel Tragut},
journal= {arXiv preprint arXiv:2205.02564},
year = {2022}
}