Multi-Word Lexical Simplification

Piotr Przybyła, Matthew Shardlow


Abstract
In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators. We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.
Anthology ID:
2020.coling-main.123
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1435–1446
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.123
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.123.pdf