Sentence Alignment Methods for Improving Text Simplification Systems

Sanja Štajner, Marc Franco-Salvador, Simone Paolo Ponzetto, Paolo Rosso, Heiner Stuckenschmidt


Abstract
We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.
Anthology ID:
P17-2016
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–102
Language:
URL:
https://www.aclweb.org/anthology/P17-2016
DOI:
10.18653/v1/P17-2016
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2016.pdf
Video:
 https://vimeo.com/234958364