SEMA: Text Simplification Evaluation through Semantic Alignment

Xuan Zhang, Huizhou Zhao, KeXin Zhang, Yiyang Zhang


Abstract
Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.
Anthology ID:
2020.nlptea-1.17
Volume:
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
NLP-TEA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–128
Language:
URL:
https://aclanthology.org/2020.nlptea-1.17
DOI:
Bibkey:
Cite (ACL):
Xuan Zhang, Huizhou Zhao, KeXin Zhang, and Yiyang Zhang. 2020. SEMA: Text Simplification Evaluation through Semantic Alignment. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 121–128, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
SEMA: Text Simplification Evaluation through Semantic Alignment (Zhang et al., NLP-TEA 2020)
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PDF:
https://aclanthology.org/2020.nlptea-1.17.pdf