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

TL;DR

This paper advocates for personalized word complexity models tailored to individual readers, demonstrating that such models outperform generic ones and providing a new dataset and active learning framework for future research.

Contribution

It introduces a novel active learning framework for creating personalized word complexity models and releases a benchmark dataset for the community.

Findings

Personalized models outperform generic models in predicting word difficulty.

Active learning effectively tailors models to individual readers.

A new dataset of complexity annotations is provided for further research.

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.