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

• URL: http://arxiv.org/abs/2205.02564v1
• Date: Thu, 5 May 2022 10:53:31 GMT
• Title: One Size Does Not Fit All: The Case for Personalised Word Complexity Models
• Authors: Sian Gooding and Manuel Tragut
• Abstract summary: Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. In this paper, we show that personal models are best when predicting word complexity for individual readers.
• Score: 4.035753155957698
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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