Abstract
A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users’ differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.- Anthology ID:
- C18-1019
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 224–232
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/C18-1019/
- DOI:
- Cite (ACL):
- John Lee and Chak Yan Yeung. 2018. Personalizing Lexical Simplification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 224–232, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Personalizing Lexical Simplification (Lee & Yeung, COLING 2018)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/C18-1019.pdf