Semantic Structural Evaluation for Text Simplification

Elior Sulem, Omri Abend, Ari Rappoport


Abstract
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA’s substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.
Anthology ID:
N18-1063
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:
685–696
Language:
URL:
https://www.aclweb.org/anthology/N18-1063
DOI:
10.18653/v1/N18-1063
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1063.pdf
Video:
 http://vimeo.com/282318359