Simplification Using Paraphrases and Context-Based Lexical Substitution

Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki, Chris Callison-Burch


Abstract
Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word identification (CWI) model that exploits both lexical and contextual features, and a simplification mechanism which relies on a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. We compare our CWI and lexical simplification models to several baselines, and evaluate the performance of our simplification system against human judgments. The results show that our models are able to detect complex words with higher accuracy than other commonly used methods, and propose good simplification substitutes in context. They also highlight the limited contribution of context features for CWI, which nonetheless improve simplification compared to context-unaware models.
Anthology ID:
N18-1019
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
207–217
Language:
URL:
https://aclanthology.org/N18-1019
DOI:
10.18653/v1/N18-1019
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N18-1019.pdf
Data
Newsela