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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-naacl.27.pdf