NAACL 2022findings15 citations
One Size Does Not Fit All: The Case for Personalised Word Complexity Models
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.
BibTeX
@inproceedings{gooding-tragut-2022-one,
title = "One Size Does Not Fit All: The Case for Personalised Word Complexity Models",
author = "Gooding, Sian and
Tragut, Manuel",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.27/",
doi = "10.18653/v1/2022.findings-naacl.27",
pages = "353--365"
}