Personalized Substitution Ranking for Lexical Simplification

John Lee, Chak Yan Yeung


Abstract
A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user’s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.
Anthology ID:
W19-8634
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Editors:
Kees van Deemter, Chenghua Lin, Hiroya Takamura
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
258–267
Language:
URL:
https://aclanthology.org/W19-8634
DOI:
10.18653/v1/W19-8634
Bibkey:
Cite (ACL):
John Lee and Chak Yan Yeung. 2019. Personalized Substitution Ranking for Lexical Simplification. In Proceedings of the 12th International Conference on Natural Language Generation, pages 258–267, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Personalized Substitution Ranking for Lexical Simplification (Lee & Yeung, INLG 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8634.pdf