One Size Does Not Fit All: The Case for Personalised Word Complexity Models

Sian Gooding, Manuel Tragut


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.
Anthology ID:
2022.findings-naacl.27
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
353–365
Language:
URL:
https://aclanthology.org/2022.findings-naacl.27
DOI:
10.18653/v1/2022.findings-naacl.27
Bibkey:
Cite (ACL):
Sian Gooding and Manuel Tragut. 2022. One Size Does Not Fit All: The Case for Personalised Word Complexity Models. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 353–365, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (Gooding & Tragut, Findings 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.findings-naacl.27.pdf